SORT vs DeepSORT vs ByteTrack vs OC-SORT: Which Tracking Algorithm Should You Use?

Object detectors such as RF-DETR identify objects in individual video frames, but they do not maintain object identities across frames. Object tracking addresses this limitation by associating detections across consecutive frames and assigning consistent IDs to objects as they move through a video.

Several object tracking algorithms exist, each using a different approach to associate detections across video frames and maintain consistent object IDs. In this guide, we compare four popular tracking algorithms: SORT, DeepSORT, ByteTrack, and OC-SORT.

You'll learn how each algorithm works, what makes it unique, its strengths and weaknesses, and when to use it. We'll also show you how to integrate these algorithms into your computer vision pipelines using Roboflow Workflows, so you can test different tracking algorithms on your own videos and choose the best fit for your application.

The Basics of Object Tracking Algorithms

SORT, DeepSORT, ByteTrack, and OC-SORT all use tracking-by-detection, a common approach to multi-object tracking. Rather than detecting and tracking objects in a single step, these algorithms take detections from an object detector, such as RF-DETR, and associate them across consecutive video frames to maintain consistent track IDs for the same objects.

A track represents an object being followed across multiple video frames and maintains a unique ID for that object. Association means matching new detections (the output of an object detector like RF-DETR) to existing tracks to determine whether they correspond to the same objects.

Although all four algorithms follow the tracking-by-detection approach, they differ in how they predict object motion, associate detections with existing tracks, and manage missing or low-confidence detections. The process generally involves four steps:

1. Detect objects: Run an object detector, such as RF-DETR, on each video frame to obtain bounding boxes and confidence scores. Detections from the first frame can be used to initialize tracks and assign IDs to objects.
2. Predict motion: For subsequent frames, use a motion model, typically based on a Kalman filter, to predict where each existing track's object is likely to appear based on its previous positions and estimated movement. Learn more about how Kalman filters work in this guide.
3. Associate detections with tracks: Compare the current frame's object detections with the positions predicted by the motion model to determine which detections correspond to existing tracks. Depending on the algorithm, this process may also consider appearance features or detection confidence.
4. Manage tracks: Update existing tracks using matched detections, initialize new tracks for eligible unmatched detections, and retain or remove unmatched tracks according to the algorithm's rules.

What Is SORT Tracking?

SORT (Simple Online and Realtime Tracking) is a multi-object tracking algorithm that combines a Kalman filter to predict object positions with the Hungarian algorithm to match those predictions with new bounding boxes produced by an object detector, such as RF-DETR. Learn more about the Hungarian algorithm in this guide.

This allows SORT to maintain consistent IDs for objects across consecutive video frames. Introduced in the 2016 paper Simple Online and Realtime Tracking, SORT is known for its simplicity, speed, and efficiency.

Steps of SORT Tracking

1. Detect objects using RF-DETR: RF-DETR processes the current video frame and returns bounding boxes, class labels, and confidence scores for detected objects. In the first frame, SORT initializes tracks for eligible detections and assigns them unique IDs. A track is a representation of an object being followed across video frames. It maintains a unique ID.

2. Predict object positions using a Kalman filter: Starting from the second frame, the Kalman filter predicts where each existing track's bounding box should appear based on its previous state and estimated motion. Meanwhile, RF-DETR detects objects in the current frame and returns their bounding boxes.

3. Match Kalman-predicted tracks with RF-DETR detections: SORT calculates the Intersection over Union (IoU) between Kalman-predicted bounding boxes and new RF-DETR detections to measure their overlap. It converts these IoU scores into matching costs, typically using 1 - IoU, where lower costs indicate better matches. Pairs whose IoU falls below a minimum threshold are not allowed to match.

The Hungarian algorithm uses these costs to find the best one-to-one assignment between existing tracks and detections. It does not assign IDs based on the order of RF-DETR's detections. Instead, it matches detections to the most suitable existing tracks, allowing objects to retain their IDs across consecutive frames.

4. Update tracks and manage track IDs: For each matched track, the Kalman filter updates its predicted state using the corresponding RF-DETR detection, combining the Kalman-predicted state with the new measurement to produce an updated estimate. The track retains its existing ID, and the updated estimate provides the bounding box and motion information used for continued tracking.

What Happens When SORT Cannot Match Tracks and Detections?

Not every track can be matched to a RF-DETR detection, and not every detection can be matched to an existing track. These are called unmatched detections and unmatched tracks.

An unmatched detection occurs when RF-DETR detects an object, but its bounding box does not overlap enough with any existing Kalman-predicted bounding box to meet the IoU threshold. For example, when a new person enters the frame, their detection may not match any existing track. SORT can then initialize a new track and assign it a new ID. In typical implementations, a new track is only reported after it has been matched for a minimum number of frames (the min_hits parameter), which helps filter out false positives.

An unmatched track occurs when a Kalman-predicted bounding box (stored within track's state) does not overlap enough with any RF-DETR detection to meet the IoU threshold. For example, a person may temporarily disappear behind another person, leaving no suitable detection to match their predicted track. SORT keeps the existing track active for a limited number of frames (the max_age parameter), allowing it to match the person again if they reappear. If the track remains unmatched for longer than that, SORT removes it.

What Is DeepSORT Tracking?

DeepSORT (Simple Online and Realtime Tracking with a Deep Association Metric) is a multi-object tracking algorithm that extends SORT by combining Kalman filter-based motion prediction with deep learning-based appearance features. It was introduced in the 2017 paper Simple Online and Realtime Tracking with a Deep Association Metric.

Like SORT, DeepSORT uses a Kalman filter to predict object movement and the Hungarian algorithm to match predicted tracks with new bounding boxes produced by an object detector. However, DeepSORT also uses appearance features extracted by a trained neural network to help identify the same objects across video frames, even when they overlap or move close to one another. It also uses a matching cascade that prioritizes recently updated tracks over those that have been missing for longer.

Steps of DeepSORT Tracking

1. Detect objects using RF-DETR: RF-DETR processes the current video frame and returns bounding boxes, class labels, and confidence scores for detected objects. DeepSORT uses these bounding boxes to extract appearance features from the detected objects using a trained neural network. In the first frame, DeepSORT initializes tracks for eligible detections and assigns them unique IDs.

2. Predict object positions using a Kalman filter: Starting from the second frame, the Kalman filter predicts where each existing track's bounding box should appear based on its previous state and estimated motion. Meanwhile, RF-DETR detects objects in the current frame and returns their bounding boxes.

3. Match Kalman-predicted tracks with RF-DETR detections: DeepSORT associates tracks and detections in three parts:

• Motion Gating and appearance distance: DeepSORT first uses motion information to rule out track-detection pairs that are too far apart. It measures the Mahalanobis distance between each detection and the Kalman filter's predicted position to determine whether the detection is close enough to be a possible match for that track.It then compares the appearance features extracted from each RF-DETR detection with the stored appearance features of each existing track to calculate appearance (cosine) distances. Lower distances indicate greater visual similarity between objects. These distances help DeepSORT determine which detections belong to which tracks. Pairs that fail the motion gating threshold or the appearance distance threshold are excluded from matching. The remaining pairs are passed to the matching cascade for track-detection association.
• Matching cascade: Instead of matching all confirmed tracks (tracks that have received enough detections to be considered reliable) at once, DeepSORT matches them to detections in order of how recently they were last matched. Tracks matched in the previous frame are processed first, followed by tracks that missed one frame, then two frames, and so on.Within each group (tracks grouped by the number of frames since their last successful match), the Hungarian algorithm finds the best one-to-one assignment using appearance distances as the matching cost. This prioritizes recently matched tracks over tracks that have been missing for longer. The original paper also describes a weighted combination of motion and appearance distances as an alternative matching cost.
• IoU matching: After the matching cascade, DeepSORT uses the Hungarian algorithm again, this time using bounding-box IoU as the matching cost. It attempts to match the remaining unmatched detections with two kinds of tracks: unconfirmed tracks (newly initialized tracks that have not yet received enough detections to be confirmed), and confirmed tracks that were matched in the previous frame but failed to match during the cascade in the current frame. This gives these tracks and detections another opportunity to be matched based on bounding-box overlap rather than appearance similarity. Tracks and detections already matched in the matching cascade are not considered again.

4. Update tracks using matched detections: For each matched track, the Kalman filter updates its predicted state using the corresponding RF-DETR detection. DeepSORT also stores the detection's appearance features in the track's appearance-feature history, helping it recognize the same object in subsequent frames. The track retains its existing ID, and the updated state provides the bounding box and motion information used for continued tracking.

What Happens When DeepSORT Cannot Match Tracks and Detections?

Like SORT, DeepSORT can encounter unmatched tracks and unmatched detections. However, DeepSORT differs in how it matches objects and in how it formalizes track creation.

For an unmatched detection, DeepSORT initializes a new tentative track and assigns it a new ID. A tentative track is a provisional track that has not yet been confirmed as a real object. It can attempt to match detections in subsequent frames, including during the Intersection over Union (IoU)-based matching stage. If it fails to match a detection while still tentative, DeepSORT deletes the track. This helps filter out false positives, such as a spurious RF-DETR detection that appears in only one frame.

If the tentative track instead keeps receiving matching detections for enough consecutive frames, it becomes a confirmed track. Only confirmed tracks are considered reliable and take part in the matching cascade. Confirmed tracks are more forgiving: if one goes unmatched, DeepSORT keeps it alive for a limited number of frames (its maximum age) and deletes it only if it stays unmatched beyond that. SORT also requires a short run of successful matches before reporting a track, but DeepSORT makes this explicit with its tentative and confirmed states.

For an unmatched track, DeepSORT can keep the track active temporarily, allowing it to match the person again if they reappear. Unlike SORT, DeepSORT can use appearance features alongside motion information to associate a new detection with an existing track, even when bounding-box overlap alone is insufficient. If the track remains unmatched beyond its allowed age, DeepSORT removes it.

What Is ByteTrack Tracking?

ByteTrack is a multi-object tracking algorithm that improves tracking accuracy by using both high-confidence and low-confidence object detections when associating detections with existing tracks across video frames. It was introduced in the 2022 paper ByteTrack: Multi-Object Tracking by Associating Every Detection Box.

Like SORT, ByteTrack uses a Kalman filter to predict object positions and IoU-based matching to associate detections with existing tracks. Its key innovation is a two-stage association process: it first matches tracks with high-confidence detections, then matches the remaining unmatched tracks with low-confidence detections to recover tracks that might otherwise be lost.

Steps of ByteTrack Tracking

1. Detect objects using RF-DETR: RF-DETR processes the current video frame and returns bounding boxes, class labels, and confidence scores for detected objects. ByteTrack divides these detections into two groups based on their confidence scores: high-confidence detections and low-confidence detections. In the first frame, ByteTrack initializes tracks for eligible high-confidence detections and assigns them unique IDs.

2. Predict object positions using a Kalman filter: Starting from the second frame, the Kalman filter predicts where each existing track's bounding box should appear based on its previous state and estimated motion. Meanwhile, RF-DETR detects objects in the current frame and provides their bounding boxes and confidence scores.

3. Match tracks with high-confidence and low-confidence detections: ByteTrack first associates existing tracks with high-confidence detections using IoU-based matching costs and the Hungarian algorithm. Matched tracks retain their existing IDs, and their states are updated using the corresponding detections.

Next, ByteTrack matches the remaining unmatched tracks with low-confidence detections, again using IoU, typically with a different matching threshold than the first stage. These detections may represent real objects that are partially occluded, blurred, or difficult for RF-DETR to detect confidently. By matching them with unmatched tracks, ByteTrack can recover objects that might otherwise be lost. Low-confidence detections that remain unmatched are generally discarded rather than used to initialize new tracks.

4. Update tracks and manage track IDs: For each matched track, the Kalman filter updates its predicted state using the corresponding RF-DETR detection, combining the Kalman-predicted state with the new measurement to produce an updated estimate. The track retains its existing ID, and the updated estimate provides the bounding box and motion information used for continued tracking.

What Happens When ByteTrack Cannot Match Tracks and Detections?

An unmatched high-confidence detection occurs when a detection cannot be associated with any existing track during the first association stage. If it meets the track-initialization criteria, ByteTrack can create a new track and assign it a unique ID.

An unmatched low-confidence detection occurs when a detection cannot be associated with any remaining unmatched track during the second association stage. ByteTrack generally discards these detections rather than creating new tracks from them.

An unmatched track is an existing track that cannot be associated with a detection during either association stage. ByteTrack marks it as lost and keeps it in a buffer for a limited number of frames, allowing it to match a detection in a subsequent frame. If it remains unmatched for too long, ByteTrack removes it.

What Is OC-SORT Tracking?

OC-SORT (Observation-Centric SORT) is a multi-object tracking algorithm that improves SORT by using actual object detections to guide association and correct motion estimates, rather than relying solely on Kalman filter predictions. This makes tracking more reliable under occlusion and nonlinear motion. OC-SORT was introduced in the 2023 paper Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking.

Like SORT, OC-SORT uses a Kalman filter to predict object movement and an IoU-based matching process to associate Kalman filter-predicted bounding boxes with new detections. However, OC-SORT introduces three key innovations: observation-centric momentum (OCM), which uses the direction of movement between actual observations (RF-DETR Predictions) to improve association; observation-centric recovery (OCR), which helps re-associate tracks after missed detections; and observation-centric re-update (ORU), which corrects the Kalman filter's state estimates when a lost object reappears.

How Does OC-SORT Work?

1. Detect objects using RF-DETR: RF-DETR processes each video frame and returns bounding boxes, class labels, and confidence scores for detected objects. In the first frame, OC-SORT initializes tracks for eligible detections and assigns each new track a unique ID.

2. Predict object positions using a Kalman filter: For each subsequent frame, the Kalman filter predicts where existing tracks' bounding boxes are likely to appear based on their previous states and estimated motion. Meanwhile, RF-DETR detects objects in the current frame and returns their bounding boxes.

3. Match predicted tracks with RF-DETR detections: OC-SORT calculates the Intersection over Union (IoU) between Kalman-predicted bounding boxes and new RF-DETR detections to measure their overlap. It uses these IoU scores to calculate matching costs and determine which detections may belong to existing tracks.

Unlike SORT, OC-SORT also incorporates the history of observed object positions into track association through observation-centric momentum (OCM). It works as follows:

1. Estimate the direction of movement: OC-SORT uses past RF-DETR detections associated with a track to estimate the object's direction of movement.
2. Calculate candidate directions: For each candidate RF-DETR detection, OC-SORT calculates the direction from the track's earlier observed position to the candidate detection. OC-SORT does this for every track-detection pair, producing a direction value for each possible match. These values are then combined with the IoU costs in the next step.
3. Evaluate directional consistency: OC-SORT adds a directional-consistency term to the association cost alongside IoU-based matching information. IoU measures the overlap between Kalman-predicted bounding boxes and candidate RF-DETR detections, while OCM adds information about how consistent the candidate is with the object's observed movement direction.
4. Assign detections to tracks: The Hungarian algorithm uses the combined cost for every track-detection pair, which reflects both IoU and directional information, to find the best one-to-one assignments between existing tracks and new detections.

For example, if a person was moving to the right and two candidate detections are available, OCM can favor the detection whose direction is more consistent with the person's previous movement, assuming other matching evidence is comparable. However, if the person suddenly turns left, the previous direction may favor the wrong detection. OCM improves matching in many situations but cannot guarantee the correct association.

4. Update tracks and manage track IDs: For each matched track, OC-SORT uses the corresponding RF-DETR detection to update the Kalman filter's state and retains the track's existing ID. Unmatched detections may initialize new tracks, while unmatched existing tracks can remain active temporarily without a detection. OC-SORT manages these tracks according to its lifecycle rules.

What Happens When OC-SORT Cannot Match Tracks and Detections?

For an unmatched detection, OC-SORT can initialize a new track and assign it a unique ID if the detection meets the track-initialization criteria.

For an unmatched track, OC-SORT can keep the track active temporarily while waiting for the object to reappear. OC-SORT then uses two mechanisms to handle unmatched tracks and recover their associations with detections:

1. Observation-centric recovery (OCR) performs a second association pass. After the initial matching process, OC-SORT attempts to match each still-unmatched track's last observed box (the bounding box detected by RF-DETR in the most recent frame where the object was successfully matched, rather than its Kalman-predicted box) with the remaining unmatched detections. This helps recover tracks when Kalman filter predictions drift away from an object's actual position, such as after missed detections or occlusion.If Observation-centric recovery successfully matches an unmatched track with a detection, the object can retain its existing track ID. Tracks that remain unmatched are kept temporarily to allow for re-association if the object reappears. If a track remains unmatched beyond OC-SORT's allowed lifespan, it is removed. Meanwhile, detections that remain unmatched after the association attempts can initialize new tracks and receive unique IDs if they meet the track-initialization criteria.
2. Observation-centric re-update (ORU) is applied when a lost track is re-associated with a new detection. It uses the observations from before and after the gap to build a virtual trajectory across the missed frames and re-updates the Kalman filter along it, reducing the error accumulated during the gap.

Choosing the Right Object Tracking Algorithm for Your Use Case

When to Use SORT: Fast, Lightweight Tracking

• Best for: Fast, lightweight tracking in relatively straightforward scenarios where objects move predictably and detections are consistent.
• Typical use cases: Vehicle tracking with limited occlusion, people counting, and tracking objects on conveyor belts in controlled environments.
• Why choose it: SORT combines Kalman filter-based motion prediction with IoU-based matching, making it simple and computationally efficient.
• Limitations: It can struggle when objects disappear behind obstacles or cross paths, potentially causing identity switches.

When to Use DeepSORT: Tracking with Appearance-Based Matching

• Best for: Tracking scenarios where maintaining object identities is important, especially when objects overlap, cross paths, or move close together.
• Typical use cases: Pedestrian tracking in crowded spaces, video surveillance, sports analytics, and customer movement tracking in retail environments.
• Why choose it: DeepSORT uses deep appearance features to associate detections with existing tracks, even when motion information and bounding-box overlap alone are insufficient.
• Limitations: Appearance feature extraction adds computational overhead, and similar-looking objects can still cause identity switches.

When to Use ByteTrack: Tracking Low-Confidence Detections

• Best for: Tracking scenarios where objects may be partially occluded, blurred, or detected with low confidence.
• Typical use cases: Vehicle tracking in traffic footage, pedestrian tracking in crowded public spaces, and sports analytics involving fast-moving objects.
• Why choose it: ByteTrack uses a two-stage association process. It first matches tracks with high-confidence detections, then attempts to match remaining tracks with low-confidence detections. This helps recover objects that might otherwise be lost.
• Limitations: ByteTrack does not use deep appearance features by default, so it can struggle to maintain object identities when similar-looking objects cross paths.

When to Use OC-SORT: Tracking Irregular and Unpredictable Motion

• Best for: Tracking scenarios involving sudden direction changes, temporary occlusions, or irregular object movement that can make Kalman filter predictions less reliable.
• Typical use cases: Sports player tracking, pedestrian tracking in crowded scenes, and vehicle tracking during turns or lane changes.
• Why choose it: OC-SORT uses observation-centric momentum to improve association based on observed movement direction. Its observation-centric recovery and re-update mechanisms also help recover tracks after missed detections and correct motion estimates.
• Limitations: OC-SORT does not use deep appearance features by default, so distinguishing similar-looking objects that cross paths can remain challenging.

SORT vs DeepSORT vs ByteTrack vs OC-SORT

You can also find real-world output comparisons of several other tracking algorithms in this Roboflow tracker guide.

Integrating SORT, ByteTrack, OC-SORT, and BoT-SORT into Vision Pipelines with Roboflow Workflows

Roboflow Workflows is a visual, low-code platform for building computer vision applications by connecting vision models, tracking algorithms, image processing operations, and custom logic through a drag-and-drop interface. Each component is represented as a block, allowing you to build computer vision pipelines without implementing every component from scratch.

Workflows includes object tracking blocks for algorithms such as SORT, ByteTrack, OC-SORT, and BoT-SORT, making it easy to integrate tracking into your computer vision pipelines without implementing the algorithms yourself.

You can also use Roboflow Agent, available in your workspace after you log in to Roboflow, to build object tracking workflows using natural language.

Roboflow Agent provides a conversational interface for Roboflow tools, including Workflows. Instead of manually adding and connecting blocks, you can describe the workflow you want to build, and Agent generates it for you. You can then review the generated workflow, modify individual components, and customize the processing logic in the Workflow Editor.

For example, you can ask Roboflow Agent to create a workflow that uses SORT to track objects across videos:

Roboflow Agent automatically generates the workflow and connects the components required to track objects across video frames. You can then review the generated workflow, modify its components, and adjust the processing logic to suit your specific use case. The Workflow Editor also lets you test the workflow by dragging and dropping videos into the interface.

You can access and test the generated workflow through a UI by opening it in the Workflow Editor, as shown below. Try the workflow.

Roboflow Agent can also add computer vision operations or modify an existing workflow's configuration. You can describe additional detection, classification, image processing, or post-processing steps in natural language, and Agent will add the corresponding blocks to the workflow.

In this example, I used Roboflow Agent to configure the workflow to track only motorcycles and improve detection stability using a Detection Stabilizer block.

It then updated the RF-DETR model's configuration to detect only the motorcycle class, as shown below.

It also added a Detections Stabilizer block to the workflow to help stabilize detections across video frames.

You can then run the workflow directly in the Workflow Editor to try it on a sample video.

When you run it on the sample video, the output is generated frame by frame, as shown below.

Once the entire video has been processed, you can download the output video with the tracked objects visualized and their tracking IDs displayed. Try the workflow.

Deploy Object Tracking Workflows with Roboflow Deploy

Once you have finished building your workflow, publish it to make it live and available through the API. After publishing, click </> Use in the Workflows Editor to open the deployment panel. From there, you can choose how you want to deploy your workflow in production.

Roboflow Deploy automatically generates production-ready code snippets that you can copy into your application. These snippets can also be used with AI coding assistants such as Codex, Claude, Cursor, and ChatGPT to help integrate your workflow into an existing application.

Roboflow Deploy supports cloud-based deployment through the Serverless Cloud API, where your workflow runs on Roboflow's infrastructure and you are charged credits based on inference usage.

With the Serverless Cloud API, supported open-weight models such as RF-DETR can run on Roboflow's infrastructure as part of your workflow. This means you don't need to provision or manage a dedicated GPU to run these models.

You can also deploy your workflow locally and run it on your own hardware, including devices such as NVIDIA Jetson and Raspberry Pi. This can be useful when you need to process data locally or run your application in environments with limited or no internet connectivity.

The Deployment panel also provides ready-to-use code for running your workflow with different input sources, including images, video files, live webcam streams, and RTSP camera streams.

Conclusion

SORT, DeepSORT, ByteTrack, and OC-SORT use different approaches to maintain object identities across video frames. With Roboflow Workflows, you can easily integrate these algorithms into your computer vision pipelines, and with Roboflow Agent, you can create and modify workflows simply using natural language. Run workflows on your own videos to compare tracking algorithms, evaluate their performance, and determine which one best fits your application. Start building your own object tracking pipeline with Roboflow today.