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People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead...
Human action recognition is a challenging vision task due to the complex action patterns in the real-world videos. In this work, we propose a DeepAction Kernel Gaussian Process, which takes advantage of Gaussian process (GP) and deep learning, to capture the distinctive action characteristics. Specifically, we design a unified, deep and non-adjacent kernel structure within Gaussian process to classify...
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper,...
Event recognition from still images is one of the most important problems for image understanding. However, compared with object recognition and scene recognition, event recognition has received much less research attention in computer vision community. This paper addresses the problem of cultural event recognition in still images and focuses on applying deep learning methods on this problem. In particular,...
This paper describes our method and attempt on track 2 at the ChaLearn Looking at People (LAP) challenge 2015. Our approach utilizes Fisher vector and iDT features for action spotting, and improve its performance from two aspects: (i) We take account of interaction labels into the training process; (ii) By visualizing our results on validation set, we find that our previous method [10] is weak in...
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