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Pedestrian flow estimation is a vital issue in video surveillance. Inspired by fluid mechanics we proposed to model the pedestrian flow as time-dependent fluid, and estimate the pedestrian flow using flux. Firstly, optical flow is used to construct the motion vector field. Then, we compute the inside and outside flux components within fixed areas to estimate the pedestrian flow in different direction...
Large scale, class imbalanced data classification is a challenging task that occurs frequently in several computer vision tasks such as web video retrieval. A number of algorithms have been proposed in literature that approach this problem from different perspectives (e.g. Sampling, Cost-sensitive learning, Active learning). The challenge is two fold in this task — first the data imbalance causes...
We present a detailed study of Naive Bayes Nearest Neighbor (NBNN) proposed by Boiman et al., with application to scene categorization and video event detection. Our study indicates that using Dense-SIFT along with dimensionality reduction using PCA enables NBNN to obtain state-of-the-art results. We demonstrate this on two tasks: (1) scene image categorization on the UIUC 8 Sports Events Image Dataset...
Object tracking is an important issue in video surveillance. In this paper, we present a tracking framework based on ORB (Oriented FAST and Rotated BRIEF) feature using temporal-spacial constraint. ORB is a fast binary descriptor which needs low computation cost and has similar matching performance with SIFT or SURF. Firstly, ORB keypoints and their descriptors of the object are calculated on two...
Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance,...
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