Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation

Abstract

Abstract-Detecting pedestrians is still a challenging task for automotive vision systems due to the extreme variability of targets, lighting conditions, occlusion, and high-speed vehicle motion. Much research has been focused on this problem in the last ten years and detectors based on classifiers have gained a special place among the different approaches presented. This paper presents a state-of-the-art pedestrian detection system based on a two-stage classifier. Candidates are extracted with a Haar cascade classifier trained with the Daimler Detection Benchmark data set and then validated through a part-based histogram-of-orientedgradient (HOG) classifier with the aim of lowering the number of false positives. The surviving candidates are then filtered with feature-based tracking to enhance the recognition robustness and improve the results' stability. The system has been implemented on a prototype vehicle and offers high performance in terms of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system relies on the combination of a HOG part-based approach, tracking based on a specific optimized feature, and porting on a real prototype.

Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies

“…Vehicle detection [2], [3], [4], [5], lane detection [6], [7], [8], pedestrian detection [9] and higher order tasks involving lanes and vehicles such as trajectory analysis [10], [11], [12] using different sensing modalities, is therefore an active area of research for automotive active safety systems, and in offline data analysis for naturalistic driving studies (NDS) also [13], [14], [15].…”

i-Car: An Intelligent and Interactive Interface for Driver Assistance System

“…It contains descriptors about the object which are to be detected. In this case the Pedestrians (Prioletti et al, 2013) and the Cars. A classifier was trained with a few hundred sample views of a particular object (i.e., a face or a car), called positive examples, that are scaled to the same size (say, 20x20), and negative examples -arbitrary images of the same size.…”

Body Parts Features-Based Pedestrian Detection for Active Pedestrian Protection System

“…But further research still needs to be done to determine which feature is appropriate for the detection of the human body and its parts, such as the leg and the head. Still, there is a lot of work which needs to be done to deal with the classification accuracy and the detecting speed by designing the composite structure of classifier to make full use of each feature or each body part [22]. To utilize the appropriate features of each body part and to enhance the detection precision, this paper presents a body part based pedestrian detection.…”