Pedestrian Detection and Tracking System Based on Deep-SORT, YOLOv5, and New Data Association Metrics

Razzok, Mohammed; Badri, Abdelmajid; Mourabit, Ilham El; Ruichek, Yassine; Sahel, Aïcha

doi:10.3390/info14040218

Cited by 20 publications

(5 citation statements)

References 44 publications

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“…In this study we compared the performance of both humans and the pretrained YOLOv4-tiny model for the detection of camouflaged persons. Since YOLOv4 was introduced several newer versions have appeared that either prioritize balancing the tradeoff between speed and accuracy rather than focusing on accuracy [52,53] improve the accuracy of person detection in conditions with occlusion [54][55][56][57][58][59][60][61][62] or camouflage [63][64][65], or enhance the detection of camouflaged objects in general [66]. In contrast, the study reported here was performed to investigate if YOLO can predict human detection performance for camouflaged targets.…”

Section: Limitationsmentioning

confidence: 99%

A Convolutional Neural Network as a Potential Tool for Camouflage Assessment

Van der Burg,

Toet,

Perone

et al. 2024

Preprint

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Camouflage evaluation typically involves human visual search and detection experiments that are time-consuming and expensive. Hence, there is a need for models that compute camouflage effectiveness from digital imagery. Convolutional neural networks are a powerful tool for automatic object detection and recognition. We investigated whether such a network (YOLO) can also provide a measure of camouflage effectiveness that is related to human perception. To this end, human performance measures of camouflage effectiveness such as detection time and target conspicuity were obtained in observer experiments and compared with the performance of the (standard, i.e. pre-trained) YOLO-V4-tiny algorithm. YOLO provides the probability that a detected object is correctly classified, and this is adopted as our measure of camouflage effectiveness. We compared the YOLO-predicted classification probability for a person in camouflage clothing moving through rural and urban scenes to human detection performance. Overall, camouflage effectiveness predicted by YOLO correlates with human observer performance. At close distances, YOLO’s classification performance appears sensitive to high-contrast shape-breaking elements of a camouflage pattern. This suggests that YOLO may be adapted to assess camouflage effectiveness for a wide range of applications, such as evaluating or optimizing one’s signature and predicting optimal hiding locations in a given environment. Further research is required to fully establish YOLO’s limitations and applicability for this purpose.

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Section: Limitationsmentioning

confidence: 99%

A Convolutional Neural Network as a Potential Tool for Camouflage Assessment

Van der Burg,

Toet,

Perone

et al. 2024

Preprint

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“…In recent years, the significance of pedestrian tracking in autonomous vehicles has garnered considerable attention due to its pivotal role in ensuring pedestrian safety. Existing state-of-the-art approaches for pedestrian tracking in autonomous vehicles predominantly rely on object detection and tracking algorithms, including Faster R-CNN (Region-based Convolutional Neural Network), YOLO (You Only Look Once) and SORT algorithms [1]. However, these methods encounter issues in scenarios where pedestrians are occluded or only partially visible [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

Sukkar,

Shukla,

Kumar

et al. 2024

Information

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Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning. Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.

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“…Enhancing the capability to monitor and track objects, is a primary essential topic in today's society [15]. MOT techniques are utilized in different applications like object collision avoidance and autonomous driving [16]. The MOT approach emerges from motion-color-texture tracking to motion-appearance-mixed tracking which is trained jointly with detection to simplify the process and minimize computational costs [17].…”

Section: Introductionmentioning

confidence: 99%

ResNet50-deep affinity network for object detection and tracking in videos

2024

IJATEE

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Multiple-object tracking (MOT) plays a crucial role in addressing many fundamental challenges within the fields of computer vision and video analysis. The majority of MOT methods rely on two primary processes: object detection and data association. Initially, each video frame is analyzed to detect objects, followed by a subsequent step that establishes correlations among the detected objects across multiple frames to generate their tracks. However, the data association for tracking often relies on manually defined criteria such as motion, appearance, grouping, and spatial proximity, among others. In the study, the ResNet50-deep affinity network (DAN) was introduced, which had been designed for the detection and tracking of objects in videos, including those that appear and disappear between frames. The proposed method was evaluated using the widely recognized MOT17 dataset to address MOT challenges. During the preprocessing phase, photometric distortion correction, frame expansion, and cropping were performed. The ResNet50 model was utilized to extract features. The DAN was employed to identify object appearances in video frames and to calculate their cross-frame affinities (CFA). The approach was compared with existing research, including DAN, ByteTrack, the graph neural network for simultaneous detection and tracking (GSDT), the reptile search optimization algorithm with deep learning-based multiple object detection and tracking (RSOADL-MODT), the center graph network (CGTracker), the hybrid motion model, FlowNet2-deep learning, and the super chained tracker (SCT), to validate the efficiency of theResNet50-DAN method. The ResNet50-DAN method achieved superior results, with a multiple-object tracking accuracy (MOTA) of 84.2%, an F1 score for identification metrics (IDF1) of 80.3%, 10,352 false positives (FP), and 1,284 identity switches (ID-Sw). The ResNet50-DAN method demonstrated higher MOTA compared to the existing approaches, including DAN, ByteTrack, GSDT, RSOADL-MODT, CGTracker, the hybrid motion model, FlowNet2-DL, and SCT.

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Pedestrian Detection and Tracking System Based on Deep-SORT, YOLOv5, and New Data Association Metrics

Cited by 20 publications

References 44 publications

A Convolutional Neural Network as a Potential Tool for Camouflage Assessment

A Convolutional Neural Network as a Potential Tool for Camouflage Assessment

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

ResNet50-deep affinity network for object detection and tracking in videos

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