Browsing by Subject "Pedestrian detection"

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    A Computationally Effective Pedestrian Detection using Constrained Fusion with Body Parts for Autonomous Driving
    (IEEE, 2021) Islam, Muhammad Mobaidul; Newaz, Abdullah Al Redwan; Tian, Renran; Homaifar, Abdollah; Karimoddini, Ali; Computer Information and Graphics Technology, School of Engineering and Technology
    This paper addresses the problem of detecting pedestrians using an enhanced object detection method. In particular, the paper considers the occluded pedestrian detection problem in autonomous driving scenarios where the balance of performance between accuracy and speed is crucial. Existing works focus on learning representations of unique persons independent of body parts semantics. To achieve a real-time performance along with robust detection, we introduce a body parts based pedestrian detection architecture where body parts are fused through a computationally effective constraint optimization technique. We demonstrate that our method significantly improves detection accuracy while adding negligible runtime overhead. We evaluate our method using a real-world dataset. Experimental results show that the proposed method outperforms existing pedestrian detection methods.
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    An Extreme Learning Machine-based Pedestrian Detection Method
    (Office of the Vice Chancellor for Research, 2013-04-05) Yang, Kai; Du, Eliza Y.; Delp, Edward J.; Jiang, Pingge; Jiang, Feng; Chen, Yaobin; Sherony, Rini; Takahashi, Hiroyuki
    Pedestrian detection is a challenging task due to the high variance of pedestrians and fast changing background, especially for a single in-car camera system. Traditional HOG+SVM methods have two challenges: (1) false positives and (2) processing speed. In this paper, a new pedestrian detection method using multimodal HOG for pedestrian feature extraction and kernel based Extreme Learning Machine (ELM) for classification is presented. The experimental results using our naturalistic driving dataset show that the proposed method outperforms the traditional HOG+SVM method in both recognition accuracy and processing speed.