This paper brings significant contributions to the field of pedestrian detection by learning probabilistic dependencies and contextual information that draw special attention to the human body characteristics and silhouette shapes and play down other irrelevant features. More precisely, we introduce the NAMC (Normalized Autobinomial Markov Channels) and study the efficiency of different configu...

In this paper we propose a framework for automatic detection, tracking and counting of pedestrians in video sequences. The detection and tracking parts are based on an integrated behavioral model for pedestrian dynamics with standard image processing techniques. The target’s counting method is based on a hierarchical clustering of pedestrian trajectories where the data representation is based o...

A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage ...

In Computer Vision techniques, the detection of any interested structures, patterns or constellations can be abstracted as object detection. Object detection has wide applications in many practical tasks, like face detection [1] and pedestrian detection [2]. It also has many successful applications in biomedical tasks. Approaches which are similar to our rosette detector are used for detecting ...

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner-Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts t...

In this paper, a multiview pedestrian detection method based on Vector Boosting algorithm is presented. The Extended Histograms of Oriented Gradients (EHOG) features are formed via dominant orientations in which gradient orientations are quantified into several angle scales that divide gradient orientation space into a number of dominant orientations. Blocks of combined rectangles with their do...

In pedestrian detection applications, one of the most popular frameworks that has received extensive attention in recent years is widely known as a ‘Hough forest’ (HF). To improve the accuracy of detection, this paper proposes a novel split function to exploit the statistical information of the training set stored in each node during the construction of the forest. The proposed split function m...

Remote detection by camera offers a versatile means for recording people activities. Relying principally on changes in video images, the method tends to fail in presence of shadows and illumination changes. This paper explores a possible remedy to these problems by using range cameras instead of conventional video cameras. As range is an intrinsic measure of object geometry, it is basically not...

In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to iden...

Multiple Object Tracking (MOT) is an important problem for its various applications. In general, approaches for MOT can be categorised into two types, sequential ones and batch ones. Sequential ones utilise observations from frames up to the time while batch ones use observations from all frames of a video. For the significant progresses achieved in the pedestrian detection field, most existing...