Reliable Multi-Object Tracking in the Presence of Unreliable Detections

Mandel, Travis; Jimenez, Mark; Risley, Emily; Nammoto, Taishi; Williams, Rebekka; Panoff, Max; Ballesteros, Meynard; Suarez, Bobbie

doi:10.48550/arxiv.2112.08345

Cited by 1 publication

(4 citation statements)

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“…Fig. 1 includes some results marked with ''others'' name, these results are taken from [1] and its FPS results are not benchmarked on our GPU device and do not include the inference time of the object detection model in calculating FPS.…”

Section: E Others' Results At Figmentioning

confidence: 99%

“…Comparison of our proposed method and various algorithms based on HOTA & FPS. ''Others'' results are taken from [1], appendix E.…”

Section: Figurementioning

confidence: 99%

“…First, this section discusses the current state of object detection development and its categories. Then, we review the state of object tracking and its progress over the years, starting from tracking by motion and IOU models [1], [5], [6] until achieving the state-of-theart performance with joint end-to-end detection and tracking networks [2], [4], [7], [8], appearance features were used to contribute to tracking algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Former MOT methods [1], [5] rely on motion and velocity states of the objects to perform the tracking task using Kalman Filter [6]. In addition, intersection over union (IoU) measurement associates objects across frames.…”

Section: Introductionmentioning

confidence: 99%

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PAE: Portable Appearance Extension for Multiple Object Detection and Tracking in Traffic Scenes

Mohamed

Chuan

2022

IEEE Access

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Multi-object tracking (MOT) is an important field in computer vision that provides a critical understanding of video analysis in various applications, such as vehicle tracking in intelligent transportation systems (ITS). Several deep learning-based approaches have been introduced to basic motion and IoU trackers by extracting appearance features to assist in challenging situations such as lossy detection and occlusion. This study proposes a portable appearance extension (PAE) for single-stage object detection to jointly detect and extract appearance embeddings using a shared model. Furthermore, a novel training framework with a single image and without re-identification annotations is presented using an augmentation module, saving a tremendous amount of human labeling effort and increasing the real-world application adoption rate. Using UA-DETRAC dataset, RetinaNet-PAE and SSD-PAE achieve comparable results with current state-of-the-art models, where RetinaNet-PAE prioritizes detection and tracking performance with a 58.0% HOTA score and 4 FPS. In contrast, SSD-PAE prioritizes latency performance with a 47.3% HOTA score and 40 FPS.

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Section: E Others' Results At Figmentioning

confidence: 99%

“…Comparison of our proposed method and various algorithms based on HOTA & FPS. ''Others'' results are taken from [1], appendix E.…”

Section: Figurementioning

confidence: 99%