Accelerate proposal generation in R-CNN methods for fast pedestrian extraction

Wang, Juncheng; Li, Guiying

doi:10.1108/el-09-2018-0191

Cited by 4 publications

(15 citation statements)

References 25 publications

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“…As shown in Table 12, it is the most advanced performance for object detection using enhanced YOLOv5. When compared to the current methods, YOLOv4 [52], SAF R-CNN [61], MNPrioriBoxes-Yolo [35], and AEMS-RPN [33], the proposed methodology performs better. Second, the proposed model's miss rate for the ETH dataset is 32.63% and mAP of 78.3% compared with 33.87% of [35] and 36.46% of [33].…”

Section: Caltechmentioning

confidence: 89%

“…Fig. 12(c) compares the enhanced YOLOv5's pedestrian detection performance to that of the following methods: YOLOv4 [52], SAF R-CNN [61], MNPrioriBoxes-Yolo [35], and AEMS-RPN [33]. On the KITTI dataset, the proposed method yields encouraging results with 79%, 68%, and 64% and mAP of 78.34%.…”

Section: Kittimentioning

confidence: 97%

“…The proposed model performs better than the baseline versions of earlier state-of-the-art YOLOv5 models. It covers the COCO 2015 and 2016 challenge winners, MNC [33] and FCIS [33], respectively. FCIS is outperformed by the improved YOLOv5 with ResNet-101-FPN backbone, according to [33].…”

Section: Classify the Item Detection As True Positive If Ioumentioning

confidence: 99%

“…It covers the COCO 2015 and 2016 challenge winners, MNC [33] and FCIS [33], respectively. FCIS is outperformed by the improved YOLOv5 with ResNet-101-FPN backbone, according to [33]. It achieved a benchmark AP of 79%, AP 50 of 68%, and AP 75 of 64%.…”

Section: Classify the Item Detection As True Positive If Ioumentioning

confidence: 99%

See 3 more Smart Citations

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Gawande¹,

Hajari²,

Golhar

2023

Preprint

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Pedestrian detection and suspicious activity recognition are notable challenges in vision-based surveillance systems. However, the accuracy of pedestrian detection is influenced by a wide range of factors, including human presence, trajectory, posture, complex background, and object deformation. In this paper, we developed a pedestrian dataset that encompasses student behavior in an institution, such as cheating, stealing lab devices, dispute, and threatening scenarios. It provides uniform and steady identification annotations of pedestrians, making it suitable for pedestrian detection, tracking, and behavior detection. In addition, we also improved detection accuracy using the enhanced YOLOv5 architecture. Next, we proposed an efficient method for detection of global and local unusual behaviors. The proposed method extracts motion features that accurately describe pedestrian motion, speed, and direction, etc. The proposed method is validated on a proposed student behavior database and three additional publicly available benchmark datasets. It gives state-of-the-art detection accuracy of 96.12% and miss rate of 6.68% compared to the existing techniques. The experimental results demonstrate a substantial performance improvement in anomalous activity recognition. The final section summarizes and discusses future research directions.

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Section: Caltechmentioning

confidence: 89%

Section: Kittimentioning

confidence: 97%

Section: Classify the Item Detection As True Positive If Ioumentioning

confidence: 99%

Section: Classify the Item Detection As True Positive If Ioumentioning

confidence: 99%

See 2 more Smart Citations

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Gawande¹,

Hajari²,

Golhar

2023

Preprint

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“…R-CNN has been proposed by Ross Girshick et al [28] where a selective search algorithm is used for finding the Region of Interest (RoI) in an image, called region proposals. Selective search algorithm extract 2000 regions from the image.…”

mentioning

confidence: 99%

Scale Invariant Mask R-CNN for Pedestrian Detection

Gawande

Hajari²,

Golhar

2020

ELCVIA

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Pedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrians helps in various utility applications such as event detection in overcrowded areas, gender, and gait classification, etc. In this domain, the most recent research is based on instance segmentation using Mask R-CNN. Most of the pedestrian detection method uses a feature of different body portions for identifying a person. This feature-based approach is not efficient enough to differentiate pedestrians in real-time, where the background changing. In this paper, a combined approach of scale-invariant feature map generation for detecting a small pedestrian and Mask R-CNN has been proposed for multiple pedestrian detection to overcome this drawback. The new database was created by recording the behavior of the student at the prominent places of the engineering institute. This database is comparatively new for pedestrian detection in the academic environment. The proposed Scale-invariant Mask R-CNN has been tested on the newly created database and has been compared with the pedestrian benchmark databases. The experimental result shows significant performance improvement in pedestrian detection as compared to the existing approaches of pedestrian detection and instance segmentation. Finally, we conclude and investigate the directions for future research.

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Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

Gawande,

Hajari,

Golhar

2024

Artif Intell Rev

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Person and suspicious activity detection is a major challenge for image-based surveillance systems. However, the accuracy of person detection is affected by several factors, such as the presence of the person, his trajectory, posture, complex background, and object distortion. In this work, we developed a person-focused dataset that includes various behaviors of students in an educational institution, such as cheating, theft of lab equipment, fights, and threatening situations. This dataset ensures consistent and standardized identification annotations for individuals, making it suitable for detection, tracking, and behavioral analysis of individuals. In addition, we have increased the detection accuracy through an improved architecture called YOLOv5 and introduced an efficient method for detecting global and local anomalous behaviors. This method extracts motion features that accurately describe the person’s movement, speed, and direction. To evaluate the effectiveness of our proposed approach, we validated it against our proposed database and publicly available benchmark datasets. Our method achieves state-of-the-art detection accuracy, namely 96.12%, with an error rate of 6.68% compared to existing methods. The empirical results show a significant improvement in anomalous activity detection. Our paper concludes with a summary and a discussion of possible future research directions.

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Accelerate proposal generation in R-CNN methods for fast pedestrian extraction

Cited by 4 publications

References 25 publications

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Scale Invariant Mask R-CNN for Pedestrian Detection

Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map

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