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Edge detection had made significant progress with the help of deep Convolutional Networks (ConvNet). ConvNet based edge detectors approached human level performance on standard benchmarks. We provide a systematical study of these detector outputs, and show that they failed to accurately localize edges, which can be adversarial for tasks that require crisp edge inputs. In addition, we propose a novel...
Spatio-Temporal Interest Point (STIP) has been widely used for human action recognition. However, the performance of the STIP based methods are still limited in realistic datasets which often include large variations in illuminations, viewpoints and camera motions. One reason of the low performance is that the STIPs only reflect the local change in videos, which is not enough to obtain stable informative...
The challenges in local-feature-based image matching are variations of view and illumination. Many methods have been recently proposed to address these problems by using invariant feature detectors and distinctive descriptors. However, the matching performance is still unstable and inaccurate, particularly when large variation in view or illumination occurs. In this paper, we propose a view and illumination...
This paper proposes a novel method for rapid and robust human detection and tracking based on the omega-shape features of people's head-shoulder parts. There are two modules in this method. In the first module, a Viola-Jones type classifier and a local HOG (Histograms of Oriented Gradients) feature based AdaBoost classifier are combined to detect head-shoulders rapidly and effectively. Then, in the...
We study the problem of robust pedestrian detection. A new descriptor, Pyramidal Statistics of Oriented Filtering (PSOF), is proposed for shape representation. Unlike one-scale gradient-based methods, the PSOF descriptor constructs an image pyramid and uses a Gabor filter bank to obtain multi-scale pixel-level orientation information. Then, locally normalized pyramidal statistics of these Gabor responses...
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