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Accurate pedestrian detection in highly crowded surveillance videos is a challenging task, since the regions of pedestrians in the videos may be largely occluded by other pedestrians. In this paper, we propose an effective part-based deep network cascade (HsNet) to solve this problem. In this model, the part-based scheme effectively restrains the appearance variations of pedestrians caused by heavy...
A discriminative multi-modality non-negative sparse (DMNS) graph model is proposed in this paper. In the model, features in each modality are first projected into the Mahalanobis space by a transformation learned for this modality, a multi-modality non-negative sparse graph is then constructed in the Mahalanobis space with shared coefficients across modalities. Both the labeled and unlabeled data...
In this paper we propose a depth recovery approach for monocular videos with or without camera motion. By combining geometric information and moving object extraction, not only the depth of background but also the depth of foreground can be recovered. Furthermore, for cases involving complex camera motion such as fast moving, translating, vertical movement, we propose a novel global motion estimation...
The growing population of seniors leads to the need for an intelligent surveillance system to ensure the safety of the elders at home. Fall is one kind of the most seriously life-threatening emergencies for elderly people. Fall detection system based on video surveillance provides an efficient solution for detecting fall events automatically by analyzing human behaviors. In this paper, we propose...
In this paper, Sparse Coding with Non-negative and Locality constraints (SCNL) is proposed to generate discriminative feature descriptions for human action recognition. The non-negative constraint ensures that every data sample is in the convex hull of its neighbors. The locality constraint makes a data sample only represented by its related neighbor atoms. The sparsity constraint confines the dictionary...
This paper proposes a method for human detection in crowded scene from static images. We introduce to combine edgelet and LBP features to obtain more discriminative representations for local area. To cope with partial occlusion, part detectors are learned using real AdaBoost in bootstrap way. Responses of part detectors are combined to form the final results. We test our approach on several common...
How to separate foreground from camera and background motions is a difficult problem for human action recognition in unconstrained environments. Although the existing interest point based methods have shown attractive results, they always come with high computational complexity and lose their power in cluttered field with camera motion. In this paper, a new spatio-temporal interest point detector...
With the increasing amount of surveillance data, moving object segmentation in the compressed domain has drawn broad attention from both academy and industry. In this paper, we propose a novel moving object segmentation method towards H.264 compressed surveillance videos. First, the motion vectors (MV) are accumulated and filtered to achieve reliable motion information. Second, considering the spatial...
We propose a framework for efficient storing and scalable browsing of surveillance video based on video synopsis. Our framework employs a novel synopsis analysis scheme named Detail-based video synopsis to generate a set of object flags to store and browse surveillance video synopsis. The main contributions of our work are: 1) highlighting important contents of surveillance video; 2) improving the...
Missing data or incomplete data are very common in statistical situations. One way to deal with missing data is to conduct model imputation either one time or multiple times. One of the key problems in analyzing the imputed dataset is to give the valid statistical reference of the parameter estimated, that is, to give a right estimation of the standard error of the interested statistic. This paper...
Human action recognition is a challenge problem in computer vision. In this paper, we propose an improved approach using kinematic features for action recognition. In this approach, we find the area that relates to action by a simple method, and select eight discriminative features derived from optical flow field to describe the dynamics of the field. The covariance matrix of the feature vectors is...
In this paper, we propose an efficient luggage searching system based on image classification and retrieval. In this system, we can register and retrieve the luggage automatically. We bring in classification to register images so as to increase the retrieval speed. The Block HSV histogram and the scale invariant feature transform (SIFT) are used for image retrieval. Experiments show that the proposed...
Paper currency recognition with good accuracy and high processing speed has great importance for banking system. How to extract high quality monetary features from currency images is a key problem in paper currency recognition. Based on the traditional local binary pattern (LBP) method, an improved LBP algorithm, called block-LBP algorithm, is proposed in this paper for characteristic extraction....
Freeway monitoring is one of the key elements in ITS (intelligent transportation system). Moving vehicle extraction is an important preprocessing step in freeway monitoring. It has great influence on the following steps, such as object tracking, classification and behavior analysis. This paper presents an effective method to extract moving objects. Our approach proved to be robust to sudden light...
We propose a novel relative orientation feature (ROF) to represent the contour or skeleton of a two-dimensional object. With the aid of ROF, the shapes of two objects with fine structures can be compared. Matching with ROF is invariant with respect to translation, rotation and scaling transforms. Experimental results on hand gesture recognition demonstrate the effectiveness and efficiency of ROF with...
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