Pedestrian detection is an important aspect of autonomous vehicle driving as recognizing pedestrians helps in reducing accidents between the vehicles and the pedestrians. In literature, feature based approaches have been mostly used for pedestrian detection. Features from different body portions are extracted and analyzed for interpreting the presence or absence of a person in a particular regi...

In this paper we present a simple approach for person detection in surveillance for static cameras. The basic idea is to train a separate classifier for each image location which has only to discriminate the object from the background at a specific location. This is a considerably simpler problem than the detection of persons on arbitrary backgrounds. Therefore, we use adaptive classifiers whic...

Despite growing attention in autonomy, there are still many open problems, including how autonomous vehicles will interact and communicate with other agents, such as human drivers and pedestrians. Unlike most approaches that focus on pedestrian detection and planning for collision avoidance, this paper considers modeling the interaction between human drivers and pedestrians and how it might inf...

This report documents the work conducted for PATH Task Order 5200 – the evaluation of sensor technologies for pedestrian detection. A survey of recent and available sensor products were selected and evaluated to assess their applicability for vehicle-based solutions. The performance characteristics and limitations of various products and technological approaches were investigated. Subsequently,...

Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact the quality of life. This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection and tracking in Intelligent Systems based on monocular vision. First, we detect the pedestrian using Integral Channel Features and...

Detecting pedestrians is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. The Region Covariance Matrix (RCM) descriptors show experimentally significantly out-performs existing feature sets for pedestrian detection. In this paper, we present an efficient features extract...

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detect...

Pedestrian detection is a vision task with many practical applications in video surveillance, road safety, autonomous driving and military. However, it is much more difficult compared to the detection of other visual objects, because of the tremendous variations in the inner region as well as the outer shape of the pedestrian pattern. In this paper, we propose a pedestrian detection approach th...

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide compleme...

There are no available datasets to evaluate integrated Pedestrian Detectors and Re-Identification systems, and the standard evaluation metric for Re-Identification (Cumulative Matching Characteristic curves) does not properly assess the errors that arise from integrating Pedestrian Detectors with Re-Identification (False Positives and Missed Detections). Real world Re-Identification systems req...