Multi-scale Pedestrian Detection Based on Receptive Field Matching

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

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Multi-scale Semantic Segmentation Enriched Features for Pedestrian Detection

2018 24th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr.2018.8545414 ◽

2018 ◽

Author(s):

Xiaolu Xie ◽

Zengfu Wang

Keyword(s):

Pedestrian Detection ◽

Semantic Segmentation ◽

Multi Scale

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Pedestrian detection algorithm based on improved muti-scale feature fusion

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012008 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012008

Author(s):

Hui Liu ◽

Keyang Cheng

Keyword(s):

Clustering Algorithm ◽

Feature Fusion ◽

Pedestrian Detection ◽

Detection Algorithm ◽

Data Sets ◽

False Detection ◽

Scale Feature ◽

Multi Scale ◽

Dilated Convolution ◽

Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

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