Extending IOU Based Multi-Object Tracking by Visual Information

Bochinski, Erik; Senst, Tobias; Sikora, Thomas

doi:10.1109/avss.2018.8639144

Cited by 169 publications

(116 citation statements)

References 25 publications

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“…As long as images are captured in high frame rate and cells are precisely detected, it is possible to use overlap intersection-over-union (IOU) to build inter-frame object associations [8,19], and then ideal tracking is performed.…”

Section: Primary Multi-cell Trackermentioning

confidence: 99%

Joint Multi-frame Detection and Segmentation for Multi-cell Tracking

Zhou

Wang

et al. 2019

Lecture Notes in Computer Science

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Tracking living cells in video sequence is difficult, because of cell morphology and high similarities between cells. Tracking-by-detection methods are widely used in multi-cell tracking. We perform multi-cell tracking based on the cell centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of cells. Detection performance of cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-cell tracking. A mitosis detection algorithm is proposed to detect cell mitosis and the cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, cells can be fine segmented in highly dense cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.

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Section: Primary Multi-cell Trackermentioning

confidence: 99%

Joint Multi-frame Detection and Segmentation for Multi-cell Tracking

Zhou

Wang

et al. 2019

Lecture Notes in Computer Science

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“…Milan et al proposed a human tracking method that is solved by continuous energy minimization 5) . Bochinski et al proposed a fast and accurate human tracking method by incorporating single object trackers 6) . Maksai et al proposed a method that iteratively builds a rich training set for human tracking 7) .…”

Section: Human Trackingmentioning

confidence: 99%

[Paper] Multiple Human Tracking with Alternately Updating Trajectories and Multi-Frame Action Features

Nishimura

Tasaka

Kawanishi

et al. 2020

MTA

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In this paper, we propose a multiple human tracking method with alternately updating trajectories and multiframe action features (MHT-MAF). Even though occlusion or motion blur occurs due to the sudden movement of the drone, ID switches are prevented by the stable MAF. In the experiments, we verified the effectiveness of the proposed method using the Okutama-Action dataset. Our code is available online (https://github.com/hitottiez/mht-paf).

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“…It has accuracy tradeoffs and shows strong performance with large size and latency. Tijtgat, N. et al [18] proposed an approach by using the drone's automatic learning algorithm to detect and track objects in real-time using the integrated camera or low-power computer system. They combine a small version of Faster-RCNN with Kernelized Correlation Filters (KCF) tracker to track one object on a drone.…”

Section: Related Workmentioning

confidence: 99%

“…International Journal of Intelligent Engineering and Systems, Vol. 13 Figure. 13 The visual results comparison between different tracking algorithms: (a) ground truth, (b) DSORT [14], (c) IOU tracker, [16] (d) KCF tracker [18], (e), and (f) the proposed algorithm using SSD and YOLO respectively…”

mentioning

confidence: 99%

Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

Abdulghafoor¹,

Abdullah²

2020

IJIES

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Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.

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Extending IOU Based Multi-Object Tracking by Visual Information

Cited by 169 publications

References 25 publications

Joint Multi-frame Detection and Segmentation for Multi-cell Tracking

Joint Multi-frame Detection and Segmentation for Multi-cell Tracking

[Paper] Multiple Human Tracking with Alternately Updating Trajectories and Multi-Frame Action Features

Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

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