Pedestrian detection is a common task in every driving assistance system. The main goal resides in obtaining a high accuracy detection in a reasonable amount of processing time. This paper proposes a novel method for superpixel-based pedestrian hypotheses generation and their validation through feature classification. We analyze the possibility of using superpixels in pedestrian detection by in...

The large number of surveillance cameras available nowadays in strategic points of major cities provides a safe environment. However, the huge amount of data provided by the cameras prevents the manual processing requiring the application of automated methods. Among such methods, pedestrian detection plays an important role in reducing the amount of data by locating only the regions of interest...

We present new achievements on the use of deep convolutional neural networks (CNN) in the problem of pedestrian detection (PD). In this paper, we aim to address the following questions: (i) Given non-deep state-of-the-art pedestrian detectors (e.g. ACF, LDCF), is it possible to improve their top performances ?; (ii) is it possible to apply a pre-trained deep model to these detectors to boost th...

Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental resul...

Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. Howe...

Pedestrians are important yet vulnerable road users, especially in urban environments. Traffic accidents involving pedestrians usually lead to serious injuries or fatalities. From an application’s point of view, pedestrian detection can be used for traffic flow monitoring, intelligent pedestrian crossing or on-board vehicle for driver assistance. From the technology’s point view, different sens...

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolut...

In the near future, we can expect on-board automotive vision systems that inform or alert the driver about pedestrians, track surrounding vehicles, and read street signs. Object detection is fundamental to the success of this type of next-generation vision system. In this paper, we present a trainable object detection system that automatically learns to detect objects of a certain class in unco...

Toward robust pedestrian counting with partly occlusion, we put forward a novel model-based approach for pedestrian detection. Our approach consists of two stages: pre-detection and verification. Firstly, based on a whole pedestrian model built up in advanced, adaptive models are dynamic determined by the occlusion condition of corresponding body parts. Thus, a heuristic approach with grid mask...

Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian det...