A fast fused part-based model with new deep feature for pedestrian detection and security monitoring

• Published in: Measurement : journal of the International Measurement Confederation 2020-02, Vol.151, p.107081, Article 107081
• Main Authors: Cheng, Eric Juwei, Prasad, Mukesh, Yang, Jie, Khanna, Pritee, Chen, Bing-Hong, Tao, Xian, Young, Ku-Young, Lin, Chin-Teng
• Format: Article
• Language: English
• Subjects: Body parts; Computation; Computer vision; Deep fused feature; Deformable partmodel; Feature maps; Formability; Haar-like feature; Knee; Machine learning; Monitoring; Monitoring systems; Pedestrian detection; Pedestrians; Security; Security monitoring; Security systems; Sensors; Spatial filtering; Support vector machines
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2019.107081

•Detect the pedestrians efficiently and accurately in the crowded environment.•Haar-like feature for different body parts to build the response feature maps.•The deep features as an input to support vector machine to detect pedestrian.•Spatial filtering, multi-ratios combination and part based to full body mehod.•Computational low and high detection accuracy with respect to the state-of-the-art. In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection.