A novel visible spectrum images-based pedestrian detection and tracking system for surveillance in non-controlled environments

Lahmyed, Redouan; Ansari, Mohamed El; Kerkaou, Zakaria

doi:10.1007/s11042-022-13026-4

Cited by 5 publications

(1 citation statement)

References 65 publications

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“…Currently, both deep learning-based and traditional interactive algorithms are commonly employed for image segmentation. Examples of these methods include U-Net segmentation [ 11 ], the multi-scale high-order attention algorithm [ 12 ], DeepLabv3+ image segmentation [ 13 ], the motion object segmentation algorithm [ 14 ], and the local alignment network model [ 15 ]. While deep learning-based segmentation algorithms can achieve accurate segmentation of target objects, they also increase the complexity of the algorithms and lead to the excessive and redundant use of computational resources when extracting low-level features repeatedly.…”

Section: Introductionmentioning

confidence: 99%

An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal

Huang,

Wang,

Gao

et al. 2023

Sensors

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In the context of predicting pedestrian trajectories for indoor mobile robots, it is crucial to accurately measure the distance between indoor pedestrians and robots. This study aims to address this requirement by extracting pedestrians as regions of interest and mitigating issues related to inaccurate depth camera distance measurements and illumination conditions. To tackle these challenges, we focus on an improved version of the H-GrabCut image segmentation algorithm, which involves four steps for segmenting indoor pedestrians. Firstly, we leverage the YOLO-V5 object recognition algorithm to construct detection nodes. Next, we propose an enhanced BIL-MSRCR algorithm to enhance the edge details of pedestrians. Finally, we optimize the clustering features of the GrabCut algorithm by incorporating two-dimensional entropy, UV component distance, and LBP texture feature values. The experimental results demonstrate that our algorithm achieves a segmentation accuracy of 97.13% in both the INRIA dataset and real-world tests, outperforming alternative methods in terms of sensitivity, missegmentation rate, and intersection-over-union metrics. These experiments confirm the feasibility and practicality of our approach. The aforementioned findings will be utilized in the preliminary processing of indoor mobile robot pedestrian trajectory prediction and enable path planning based on the predicted results.

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