Roboflow

Best Video Object Tracking Models

Compare the best 2 video object tracking models and try 1 of them on your own image, free in the Roboflow Playground. 2 are open-weight, so you can self-host them for free under their licenses.

2 models · 2 open-weight · 1 free to try · prices synced Jul 28, 2026

Open-Source Video Object Tracking Models

2 models with downloadable weights you can self-host under their licenses (Custom and MIT). 1 run live in the Playground through hosted APIs, so self-hosting is optional.

Meta
SAM 3
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.

Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
ByteDance
ByteTrack
ByteTrack is a multi-object tracking algorithm developed by ByteDance, released in October 2021. It addresses a core limitation of earlier tracking methods by associating every detection box — not just high-confidence ones — with tracklets using a two-stage matching process. Low-score detection boxes, which typically correspond to occluded or blurry objects, are retained in a secondary matching step against unmatched tracklets, significantly reducing the number of lost tracks.ByteTrack achieves strong performance on the MOT17 and MOT20 benchmarks at the time of release, operating in real time on standard GPU hardware. It is architecture-agnostic and can be paired with any object detector, making it straightforward to integrate into existing detection pipelines for video analytics, surveillance, and sports tracking applications.

How to Choose a Video Object Tracking Model

Modern tracking is mostly tracking-by-detection: a detector finds objects per frame and an association algorithm links them across frames. That means you are really choosing two things: the detector and the tracker.

Tracking quality is bounded by detection quality: missed detections become identity switches and gaps. A detector fine-tuned on your classes and camera angles (rather than a generic pretrained one) is usually the single biggest tracking upgrade available.

Motion-based trackers in the ByteTrack lineage are fast and strong when motion is smooth and occlusions are brief, which covers most fixed-camera analytics. Appearance-based re-identification (DeepSORT lineage) adds a visual embedding so identities survive longer occlusions and crossings, at extra compute cost; crowded scenes and long occlusions are where it pays.

Two pipelines with the same detection accuracy can differ wildly in identity stability. Metrics like IDF1 and HOTA weight identity consistency; if your application counts unique objects or follows individuals, those are the numbers to optimize, and a few seconds of your own footage beats any benchmark.

The bottom line: Invest in the detector first, choose motion-only association for clean scenes and appearance re-ID for crowded ones, and evaluate with identity metrics on your own footage.

Frequently Asked Questions About Video Object Tracking Models

Video object tracking is the task of following objects across the frames of a video while keeping each identity consistent through occlusions, crossings, and camera motion. The dominant pattern is tracking-by-detection: a detector finds objects per frame and an association step links them over time using motion prediction (Kalman-style) and appearance embeddings, as in ByteTrack and DeepSORT lineages. Multi-object tracking is scored with metrics like MOTA, IDF1, and HOTA that penalize both missed objects and identity switches. Tracking underpins traffic analytics, sports statistics, retail footfall analysis, and security review. This page lists 2 video object tracking models, including 2 open-weight options you can self-host; 1 of them run live in the Playground so you can test them on your own images.

It depends on your task and constraints. For fixed categories in production, a model fine-tuned on your own data typically beats any general-purpose model. Compare the video object tracking models on this page and try them on your own image to see which fits.

Yes. 2 of the 2 video object tracking models here are open-weight (for example SAM 3 and ByteTrack), free to self-host under their licenses (Custom and MIT).

Yes. You can run 1 of them in the Roboflow Playground for free. Upload an image and compare the models' output side by side, no setup required.

This page lists both video object tracking models in the Roboflow Playground catalog, all of them open-weight and free to self-host; 1 of them run live in the Roboflow Playground on your own images. Compare licenses, parameters, and release dates side by side, or open any model page for full details.