With the aim of reducing traffic accidents, vehicle-infrastructure cooperative driving safety support systems (Fig. 1) have been actively developed. These systems comprise a sensor installed on the roadside to detect nearby pedestrians, bicycles or other objects and warn approaching vehicle drivers. The sensor must accurately detect pedestrians or other objects in all environments 24 hours a da...

The approach investigated in this work employs three-dimensional LADAR measurements to detect and track pedestrians over time. The sensor is employed on a moving vehicle. The algorithm quickly detects the objects which have the potential of being humans using a subset of these points, and then classifies each object using statistical pattern recognition techniques. The algorithm uses geometric ...

Cumulative foot pressure images represent the 2D ground reaction force during one gait cycle. Biomedical and forensic studies show that humans can be distinguished by unique limb movement patterns and ground reaction force. Considering continuous gait pose images and corresponding cumulative foot pressure images, this paper presents a cascade fusion scheme to represent the potential connections...

The problem of pedestrian detection in image and video frames has been extensively investigated in the past decade. However, the low performance in complex scenes shows that it remains an open problem. In this paper, we propose to cascade simple Aggregated Channel Features (ACF) and rich Deep Convolutional Neural Network (DCNN) features for efficient and effective pedestrian detection in comple...

Accurate pedestrian detection is important for many future technologies. Detection based on vision data is widely used in many applications today, however it seems like the maximum accuracy that can be achieved has almost been reached. Therefore, other ways of improving detection such as using depth data have to be considered. In this thesis I will describe the Integral Channel Features detecto...

This paper describes a method for pedestrian detection, identification and tracking using image information. The method makes use of two cameras with a shared field of view and is robust to changes in illumination and shadows. After a brief calibration process, in which the scene is divided coarsely into planar pieces (which are later optimised), the process requires no interaction and automati...

We develop a method that can detect humans in a single image based on a new cascaded structure. In our approach, both the rectangle features and 1-D edge-orientation features are employed in the feature pool for weak-learner selection, which can be computed via the integral-image and the integral-histogram techniques, respectively. To make the weak learner more discriminative, Real AdaBoost is ...

In this paper, we propose a novel feature named histogram of template (HOT) for human detection in still images. For every pixel of an image, various templates are defined, each of which contains the pixel itself and two of its neighboring pixels. If the texture and gradient values of the three pixels satisfy a predefined formula, the central pixel is regarded to meet the corresponding template...

In this paper, we propose an ensemble classification approach to the Pedestrian Detection (PD) problem, resorting to distinct input channels and Convolutional Neural Networks (CNN). This methodology comprises two stages: piq the proposals extraction, and piiq the ensemble classification. In order to obtain the proposals, we apply several detectors specifically developed for the PD task. Afterwa...

We present a novel pedestrian detection system based on probabilistic component assembly. A part-based model is proposed which uses three parts consisting of head-shoulder, torso and legs of a pedestrian. Components are detected using histograms of oriented gradients and Support Vector Machines (SVM). Optimal features are selected from a large feature pool by boosting techniques, in order to ca...