MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Dendorfer, Patrick; Ošep, Aljoša; Milan, Anton; Schindler, Konrad; Cremers, Daniel; Reid, Ian; Roth, Stefan; Leal-Taixé, Laura

doi:10.1007/s11263-020-01393-0

Cited by 222 publications

(152 citation statements)

References 144 publications

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“…The authors set a bottom center of the 2D bounding box for pedestrians to identify the location of these in the image. To evaluate the method, MOT16 and MOT20 datasets were employed [94].…”

Section: Trajectory Prediction Based On Convolutional Neural Networkmentioning

confidence: 99%

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sighencea

Stanciu

Căleanu

2021

Sensors

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Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

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“…The authors set a bottom center of the 2D bounding box for pedestrians to identify the location of these in the image. To evaluate the method, MOT16 and MOT20 datasets were employed [94].…”

Section: Trajectory Prediction Based On Convolutional Neural Networkmentioning

confidence: 99%

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sighencea

Stanciu

Căleanu

2021

Sensors

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“…To evaluate the tracking performance, we utilize the evaluation metrics provided by MOT Challenge Benchmark [38]. First, we adopt the multi-object tracking accuracy (MOTA) to evaluate the robustness of our algorithm.…”

Section: Evaluation Metricsmentioning

confidence: 99%

SORT-YM: An Algorithm of Multi-Object Tracking with YOLOv4-Tiny and Motion Prediction

et al. 2021

Electronics

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Multi-object tracking (MOT) is a significant and widespread research field in image processing and computer vision. The goal of the MOT task consists in predicting the complete tracklets of multiple objects in a video sequence. There are usually many challenges that degrade the performance of the algorithm in the tracking process, such as occlusion and similar objects. However, the existing MOT algorithms based on the tracking-by-detection paradigm struggle to accurately predict the location of the objects that they fail to track in complex scenes, leading to tracking performance decay, such as an increase in the number of ID switches and tracking drifts. To tackle those difficulties, in this study, we design a motion prediction strategy for predicting the location of occluded objects. Since the occluded objects may be legible in earlier frames, we utilize the speed and location of the objects in the past frames to predict the possible location of the occluded objects. In addition, to improve the tracking speed and further enhance the tracking robustness, we utilize efficient YOLOv4-tiny to produce the detections in the proposed algorithm. By using YOLOv4-tiny, the tracking speed of our proposed method improved significantly. The experimental results on two widely used public datasets show that our proposed approach has obvious advantages in tracking accuracy and speed compared with other comparison algorithms. Compared to the Deep SORT baseline, our proposed method has a significant improvement in tracking performance.

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“…Still, some of the videos may not encompass a bounding box. Some visual object tracking datasets found include the visual object tracking VOT and the multiple target tracking MOT [7,8], yearly challenges which are small and accurately chosen to provide various solutions for common difficult object tracking problems such as size and illumination variation or occlusions. Other temporal localized and segment level annotated datasets such Sports-1M, consist of one million videos of sports varieties [9,10].…”

Section: Related Workmentioning

confidence: 99%

Indexed Dataset from YouTube for a Content-Based Video Search Engine

Adly

Hegazy

Elarif

et al. 2021

IJICIS

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Numerous researches on content-based video indexing and retrieval besides video search engines are tied to a large-scaled video dataset. Unfortunately, reduction in open-sourced datasets resulted in complications for novel approaches exploration. Although, video datasets that index video files located on public video streaming services have other purposes, such as annotation, learning, classification, and other computer vision areas, with little interest in indexing public video links for purpose of searching and retrieval. This paper introduces a novel large-scaled dataset based on YouTube video links to evaluate the proposed content-based video search engine, gathered 1088 videos, that represent more than 65 hours of video, 11,000 video shots, and 66,000 unmarked and marked keyframes, 80 different object names used for marking. Moreover, a state-of-the-art features vector, and combinational-based matching, beneficial to the accuracy, speed, and precision of the video retrieval process. Any video record in the dataset is represented by three features: temporal combination vector, object combination vector with shot annotations, and 6 keyframes, sideways with other metadata. Video classification for the dataset was also imposed to expand the efficiency of retrieval of video-based queries. A two-phased approach has been used based on object and event classification, storing video records in aggregations related to feature vectors extracted. While object aggregation stores video records with the maximal occurrence of extracted object/concept from all shots, event aggregation classify based on groups according to the number of shots per video. This study indexed 58 out of 80 different object/concept categories, each has 9 shot number groups.

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MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

Cited by 222 publications

References 144 publications

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

SORT-YM: An Algorithm of Multi-Object Tracking with YOLOv4-Tiny and Motion Prediction

Indexed Dataset from YouTube for a Content-Based Video Search Engine

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