Real-time high-precision pedestrian tracking: a detection–tracking–correction strategy based on improved SSD and Cascade R-CNN

Yang, Shudi; Chen, Zhehan; Xiao-ming, MA; Zong, Xianhui; Feng, Zhipeng

doi:10.1007/s11554-021-01183-y

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…Further experiments showed that the algorithm proposed by the research institute had higher recognition accuracy on all categories of targets compared to current advanced algorithms, with an average of 47.67% and 79.56% higher than the Corner-Net algorithm and Faster R-CNN algorithm. This aligned with [20] and highlighted the value of fusion for handling WST challenges. The proposed algorithm also had a shorter response time, with the best response time reaching 0.62 s, and its performance was consistent with [18], which highlighted the value of its improved detection speed for WST.…”

Section: Discussionsupporting

confidence: 52%

“…By improving its original framework, the accuracy rate and recall rate of lightweight target detection were effectively improved, and the computational complexity is reduced [19]. Aiming at the current low realtime performance and accuracy of pedestrian tracking, Yang S. et al proposed a multi-stage target detection model combined with SSD algorithm, and the experimental results proved that the model had excellent real-time performance, high stability and adaptability [20].…”

Section: Related Workmentioning

confidence: 99%

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Visual Communication Design of Weak and Small Target Images Based on Image Processing Model and Data Fusion

2024

Teh. vjesn.

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The essence of computer vision technology is to combine computers with image data to enhance their understanding and perception abilities. One of the major research hotspots in computer vision technology is the object recognition, and in the field of object recognition, a major challenge is the recognition of weak and small target images. In response to the current difficulty in detecting weak and small target images, a visual communication design with image processing models and data fusion was proposed. The Single Shot MultiBox Detector was improved to construct a weak and small target detection model, and the feature fusion method was combined to enhance its weak and small target detection ability. The ablation experiment showed that in contrast with the original model, the improved model improved the detection ability of weak and small targets by 26.54%, and the overall accuracy improved by 11.05%. Two other advanced algorithms were selected for comparison with the research algorithm, and the accuracy of the research algorithm was better, with a higher accuracy of 47.67% -79.56% than the comparison algorithm. The response time was shorter, reaching 0.62 seconds. Visual communication time and success rate performed better, with a communication time lead of 9 s to 19 s and a success rate lead of 16% to 27%. In summary, the algorithm proposed by the research institute has higher accuracy in detecting small and weak images, as well as higher visual communication efficiency and success rate, which has stronger practical significance in the field of computer vision.

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Section: Discussionsupporting

confidence: 52%

Section: Related Workmentioning

confidence: 99%