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Human detection in RGB-D images is an important yet very challenging task in computer vision. In this paper, we propose a novel human detection approach in RGB-D images, which integrates ROI (region-of-interest) generation, depth-size relationship estimation and a human detector. Our approach has the following advantages: 1) ROI generation and depth-size relationship estimation take full advantage...
In this paper, we propose an accelerated local feature extraction in a reuse scheme for action recognition. Most local features of the previous frame could be reused due to the high correlation between successive frames. Feature extraction is only needed to be applied partially in the current frame. The full-frame features of the current frame are combined by features extracted at different times...
We present a novel descriptor algorithm (DUDE) using line/point duality and a randomization strategy that provides simple but robust, consistent feature extraction and correspondence. Using duality enables us to effectively capture a distribution of line segments, and the proposed randomization strategy improves repeatability over existing techniques by generating more line features in common between...
Starting from an object's location in a video frame, tracking-by-detection methods find the location of that object in a subsequent video frame. The tracker's detection step may produce multiple false positives during short-term occlusions, which can result in loss of track. We propose a tracking-by-detection method that is robust to short-term occlusions and false positives. Here, we extend the Struck...
A two stages car detection method using deformable part models with composite feature sets (DPM/CF) is proposed to recognize cars of various types and from multiple viewing angles. In the first stage, a HOG template is matched to detect the bounding box of the entire car of a certain type and viewed from a certain angle (called a t/a pair), which yields a region of interest (ROI). In the second stage,...
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