This survey focuses on some of the most important and recent algorithms for pedestrian detection in video sequences taken from a camera mounted on a car. The longterm goal of this line of research is to develop vision-based systems that could be successfully implemented in cars for driver support in urban scenes or for autonomous driving. Some of the surveyed systems have already proved their e...

In this paper, we implement the arctangent computation in Histograms of Oriented Gradients (HOG) descriptor extraction, targeting realtime pedestrian recognition System-on-Chip (SoC) design. Experimental results with 90nm CMOS technology shows the feasibility of the hardware architecture for SoC design.

We propose a novel two-stage pedestrian detection framework that combines multiscale decorrelated filters to extract more discriminative features and a novel group costsensitive boosting algorithm. The proposed boosting algorithm is based on mixture loss to alleviate the influence of annotation errors in training data and explores varying cost for different types of misclassification. Experimen...

The paper presents an original approach for pedestrian detection using the neural network classifier called Concurrent Self-Organizing Maps (CSOM), previously introduced by first author; it represents a winner-takes-all collection of neural modules. The algorithm has the following stages: (a) feature selection using one of the three candidate techniques Histogram of Oriented Gradients (HOG)/1D ...

Among Advanced Driver Assistance Systems (ADAS) pedestrian detection is a common issue due to the vulnerability of pedestrians in the event of accidents. In the present work, a novel approach for pedestrian detection based on data fusion is presented. Data fusion helps to overcome the limitations inherent to each detection system (computer vision and laser scanner) and provides accurate and tru...

We present a vision-based pedestrian tracking system targeting a very specific application: avoiding accidents in the blind spot zone of trucks. Existing blind spot safety systems do not offer a complete solution to this problem. Therefore we propose an active alarm system, which warns the truck driver if vulnerable road users occur in the blind spot zone. Our system is based solely on a vision...

In this paper, we propose a novel method with the multi-view Bayesian network (MBN) model to detect pedestrians from multi-camera surveillance videos. In our method, the ground plane is discretized in a predefined set of locations and our aim is to estimate the occupancy probability of each location that can be then used to predict the occurrence of pedestrians. To reduce the possible phantoms,...

Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and nighttime. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy...

In this paper, we explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identific...

A method for pedestrian detection from real world outdoor scenes is presented in this paper. The technique uses disparity information, ground plane estimation and biometric information based on the golden ratio. It can detect pedestrians even in the presence of severe occlusion or a lack of reliable disparity data. It also makes reliable choices in ambiguous areas since the pedestrian regions a...