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In this paper, a novel model of pedestrian tracking by using object-based attention and human knowledge is presented. The selective units in the system are the objects and groupings which are space-driven as well as feature-driven. The factors of speed, motion direction and spatial location are used to cluster and form the groupings. Hierarchical selectivity of attention for objects in a grouping...
This paper presented a video moving object segmentation and tracking system based on the active contour and the color classification models. First, the active contour model is applied to segment the target object in the initial frame. From the segmented object, the object and background regions are extracted. Then the object and the background regions are separately clustered according to color feature...
In this paper, we propose a macro-observation scheme for unusual event detection in daily life, where motions in time-space domain are described by a global representation and individual activities do not have to be defined and modeled beforehand. The proposed representation records the time-space energy of motions of all moving objects in a scene without segmenting individual object parts or tracking...
The camera of traffic system takes numerous photos every day. It's critically important for judging kinds of car movements and intelligent transportation to pick up the outlines of automobiles and fix their positions. However, the popular car positioning algorithm is unable to reach the required fast speed in this field. In this paper we will provide readers with an algorithm to locate cars based...
This paper proposes a new approach to describe traffic scene including vehicle collisions and vehicle anomalies at intersections by video processing and motion statistic techniques. The research mainly targets on extracting abnormal event characteristics at intersections and learning normal traffic flow by trajectory clustering techniques. Detecting and analyzing accident events are done by observing...
This paper presents a novel slice-based approach to detect pedestrians in still images. A pedestrian is divided into limited numbers of slice-based sub-regions through a spatio-temporal slice processing. First, sub-regions of interest are detected in different spatio-temporal slice images. Then, a clustering algorithm is proposed to combine these sub-regions into individual pedestrians based on their...
This paper presents a new approach to trajectory-based abnormal behavior detection (ABD). While existing techniques include position in the feature vector, we propose to estimate the probability distribution locally at each position, hence reducing the dimensionality of the feature vector. Local information derived from accumulated knowledge for a particular position is integrated in the distribution...
Detecting and segmenting moving objects in dynamic scenes is a hard but essential task in a number of applications such as surveillance. Most existing methods only give good results in the case of persistent or slowly changing background, or if both the objects and the background are rigid. In this paper, we propose a new method for direct detection and segmentation of foreground moving objects in...
Kernel principal component analysis (KPCA) has gained much attention for capturing nonlinear image features which is particularly important for clustering high-dimensional multi-class features. We introduce KPCA in this paper for detecting and classifying moving vehicles from its viewpoint images. The KPCA extracts non-linear features of multi-class moving vehicles by mapping input space to a higher...
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