Model Adaption Object Detection System for Robot

Fu, Jiao; Zong, Licheng; Li, Yinbing; Li, Ké; Yang, Bian; Liu, Xibei

doi:10.23919/ccc50068.2020.9189674

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…Tracking by detection. With the rapid development of object detection [49,26,48,34,10,22,57,55], more and more methods begin to use more powerful detectors to obtain higher tracking performance. The one-stage object detector RetinaNet [34] begin to be used by several methods such as [38,47].…”

Section: Object Detection In Motmentioning

confidence: 99%

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Zhang¹,

Sun²,

Jiang³

et al. 2021

Preprint

161

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Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 score ranging from 1 to 10 points. To put forwards the state-of-theart performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU. The source code, pre-trained models with deploy versions and tutorials of applying to other trackers are released at https://github.com/ifzhang/ByteTrack.

show abstract

Section: Object Detection In Motmentioning

confidence: 99%

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Zhang¹,

Sun²,

Jiang³

et al. 2021

Preprint

161

View full text Add to dashboard Cite

show abstract

“…Tracking by detection. With the rapid development of object detection [41,22,40,29,8,18,49,47], more and more methods begin to use more powerful detectors to obtain higher tracking performance. The one-stage object detector RetinaNet [29] begin to be used by several methods such as [32,39].…”

Section: Object Detection In Motmentioning

confidence: 99%

VariabilityTrack:Multi-Object Tracking with Variable Speed Object Movement

Luo¹,

Wei²,

Lin³

2022

Preprint

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Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods can be roughly classified as tracking-by-detection and joint-detection-association paradigms. Although the latter has elicited more attention and demonstrates comparable performance relative than the former, we claim that the tracking-by-detection paradigm is still the optimal solution in terms of tracking accuracy,such as ByteTrack [70],which achieves 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU.However, under complex perspectives such as vehicle and UAV acceleration, the performance of such a tracker using uniform Kalman filter will be greatly affected, resulting in tracking loss.In this paper, we propose a variable speed Kalman filter algorithm based on environmental feedback and improve the matching process, which can greatly improve the tracking effect in complex variable speed scenes while maintaining high tracking accuracy in relatively static scenes. Eventually, higher MOTA and IDF1 results can be achieved on MOT17 test set than ByteTrack[70]

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“…The detection algorithm is the cornerstone of the operation of the TDB paradigm. The addition of the convolutional neural network (CNN) [11] enabled the detection algorithms to achieve rapid development in both detection accuracy and running speed [12][13][14][15][16][17][18][19]. Powerful detection algorithms could be introduced to TDB trackers to obtain better tracking performance.…”

Section: Tracking By Detectionmentioning

confidence: 99%

Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking

Zhou

Zhu

et al. 2022

Applied Sciences

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In order to reduce the negative impact of severe occlusion in dense scenes on the performance degradation of the tracker, considering that the head is the highest and least occluded part of the pedestrian’s entire body, we propose a new multiobject tracking method for pedestrians in dense crowds combined with head tracking. For each frame of the video, a head tracker is first used to generate the pedestrians’ head movement tracklets, and the pedestrians’ whole body bounding boxes are detected at the same time. Secondly, the degree of association between the head bounding boxes and the whole body bounding boxes are calculated, and the Hungarian algorithm is used to match the above calculation results. Finally, according to the matching results, the head bounding boxes in the head tracklets are replaced with the whole body bounding boxes, and the whole body motion tracklets of the pedestrians in the dense scene are generated. Our method can be performed online, and experiments suggested that our method effectively reduces the negative effects of false negatives and false positives on the tracker caused by severe occlusion in dense scenes.

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Model Adaption Object Detection System for Robot

Cited by 11 publications

References 20 publications

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

VariabilityTrack:Multi-Object Tracking with Variable Speed Object Movement

Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking

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