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A novel approach for deep pedestrian detection based on changes in camera viewing angle

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Abstract

Pedestrian detection based on Deep Convolutional Neural Networks (DCNNs) has made great stride in the last few years. Researchers have recently employed different DCNN-based techniques to detect pedestrian more accurately than before. In this paper, we propose a new Deep Model based on Changes in Camera Viewing Angle (DM-CCVA) to detect pedestrian. The proposed novel DM-CCVA is based on integrating a modified Single-Shot Detector (SSD) and a set of parallel Fast Region-based Convolutional Neural Networks (FRCNNs) to accurately detect pedestrian. The proposed deep architecture extracts initial candidate pedestrians using a modified SSD model, while utilizing five parallel Fast RCNNs to detect pedestrians in five different sets of camera viewing angles. We also propose a new training approach based on changes in camera viewing angle which searches the best Region of Interests (RoIs). Moreover, by exploiting a secure border in each initial candidate pedestrian, the proposed method both creates an Extended Region of Candidate Pedestrian (ERCP) and extracts multi-RoIs. It then selects a number of RoIs within the ERCP as detected pedestrians which satisfy few reasonable criteria. Comprehensive experimental results demonstrate that the proposed DM-CCVA is a highly effective method that achieves very competitive performance on two most popular pedestrian detection datasets: Caltech-USA and INRIA.

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Authors and Affiliations

  1. Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Mahmoud Saeidi & Ali Ahmadi

  2. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Ali Ahmadi

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  1. Mahmoud Saeidi
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  2. Ali Ahmadi
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Correspondence to Mahmoud Saeidi.

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Saeidi, M., Ahmadi, A. A novel approach for deep pedestrian detection based on changes in camera viewing angle. SIViP 14, 1273–1281 (2020). https://doi.org/10.1007/s11760-020-01662-y

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