In their paper, "MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors," authors Yuang Zhang, Tiancai Wang and Xiangyu Zhang propose a simple yet effective pipeline for end-to-end multi-object tracking (MOT) using a pretrained object detector. Existing end-to-end methods such as MOTR and TrackFormer have been shown to be inferior to tracking-by-detection counterparts due to poor detection performance. The proposed pipeline, MOTRv2, adopts the anchor formulation of queries and uses an additional object detector to generate proposals as anchors. This provides detection prior to MOTR and greatly eases the conflict between joint learning detection and association tasks in MOTR. The authors demonstrate that this simple modification improves the overall performance of MOTR while maintaining its end-to-end feature. MOTRv2 is evaluated on large scale benchmarks including DanceTrack and BDD100K datasets. The pipeline achieves state of the art performance on both datasets with 73.4% HOTA on DanceTrack ranking first in the 1st Multiple People Tracking in Group Dance Challenge. The authors hope that their approach can provide new insights into the end to end MOT community. Code for implementing MOTRv2 is available at https://github.com/megvii-research/MOTRv2. Overall, this paper presents a promising solution for improving end to end multi object tracking using pretrained object detectors.
- Authors propose a pipeline for end-to-end multi-object tracking (MOT) using a pretrained object detector
- Existing end-to-end methods have poor detection performance compared to tracking-by-detection counterparts
- Proposed pipeline, MOTRv2, uses an additional object detector to generate proposals as anchors and adopts the anchor formulation of queries
- This modification improves overall performance of MOTR while maintaining its end-to-end feature
- MOTRv2 achieves state-of-the-art performance on DanceTrack and BDD100K datasets with 73.4% HOTA on DanceTrack ranking first in the 1st Multiple People Tracking in Group Dance Challenge
- Code for implementing MOTRv2 is available at https://github.com/megvii-research/MOTRv2
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Definitions- Pipeline: A series of steps or actions that are taken in order to achieve a particular result.
- End-to-end: A process that covers all stages, from beginning to end.
- Pretrained: A computer program that has already been trained to do something before being used for a specific task.
- Object detector: A computer program that can identify and locate objects within an image or video.
- Tracking-by-detection: A method of object tracking where the object detector is used to detect and track objects over time.
- Proposals: Possible locations of an object within an image or video suggested by an object detector.
- Anchors: Fixed reference points used for comparison with proposals generated by an object detector.
- Formulation: The process of creating or developing something such as a plan or idea.
- Performance: How well something works or how successful it is in achieving its intended purpose.
- State-of-the-art: The most advanced level of development achieved in a particular field or technology.
MOTRv2: Boostrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
In their paper, "MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors," authors Yuang Zhang, Tiancai Wang and Xiangyu Zhang propose a simple yet effective pipeline for end-to-end multi-object tracking (MOT) using a pretrained object detector. Existing end-to-end methods such as MOTR and TrackFormer have been shown to be inferior to tracking-by detection counterparts due to poor detection performance. The proposed pipeline, MOTRv2, adopts the anchor formulation of queries and uses an additional object detector to generate proposals as anchors. This provides detection prior to MOTR and greatly eases the conflict between joint learning detection and association tasks in MOTR.
Background on End To End Multi Object Tracking
Multi object tracking is an important task in computer vision that involves detecting multiple objects in a video frame or sequence of frames and then associating them across time steps. Traditional approaches involve two separate stages - first detecting objects in each frame followed by linking them together over time. However, this approach can suffer from errors propagating through both stages which leads to suboptimal results. Recently, there has been much research into developing end to end solutions for multi object tracking that combine both the detection and association tasks into one unified model. These models are usually trained with large datasets containing videos annotated with bounding boxes around all objects present at each frame step.
Overview of MOTRv2 Pipeline
The authors propose a new pipeline for improving existing end to end multi object tracking methods called MOTRv2 which utilizes an additional pretrained object detector alongside the original model's query anchor formulation for generating proposals as anchors before performing the actual tracking process itself. This provides more accurate detections prior to running the actual tracker which helps reduce errors caused by incorrect associations during training while still maintaining its overall end to end feature set without introducing any extra complexity or computational overhead compared with other existing solutions such as TrackFormer or MOTR .
Evaluation Results
The authors evaluated their proposed pipeline on two large scale benchmarks including DanceTrack and BDD100K datasets where they achieved state of the art performance on both datasets with 73.4% HOTA on DanceTrack ranking first in the 1st Multiple People Tracking in Group Dance Challenge . The results demonstrate that this simple modification improves overall performance while still maintaining its original featureset without introducing any extra complexity or computational overhead compared with other existing solutions such as TrackFormer or MOTR . Code for implementing MOTRv2 is available at https://github.com/megvii-research/MOTRv2 .
Conclusion
Overall, this paper presents a promising solution for improving end to end multi object tracking using pretrained object detectors which can provide new insights into the field of computer vision research related topics such as motion estimation , 3D reconstruction , etc..
