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In this paper, we present a real-time and energy-efficient multi-scale object detector using Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classification. Parallel detectors with balanced workload are used to enable processing of multiple scales and increase the throughput such that voltage scaling can be applied to reduce energy consumption. Image pre-processing is...
Classification of actions by human actors from video enables new technologies in diverse areas such as surveillance and content-based retrieval. We propose and evaluate alternative models, one based on feature-level fusion and the second on decision-level fusion. Both models employ direct classification - inferring from low-level features the nature of the action. Interesting points are assumed to...
This paper presents a method for recognizing scene categories based on multiple channels of Pyramid Histogram Of Words (PHOW). The main difference among different channels lies in what kind of feature detector/descriptor pair is employed in the framework of Bag-of-Words (BoW) models. This technique works by obtaining the confidence scores of a test image belonging to each possible category based on...
A real-time and accurate human detector, C4, is proposed in this paper. C4 achieves 20 fps speed and state-of-the-art detection accuracy, using only one processing thread without resorting to special hardwares like GPU. Real-time accurate human detection is made possible by two contributions. First, we show that contour is exactly what we should capture and signs of comparisons among neighboring pixels...
The combination of local features, complementary feature types, and relative position information has been successfully applied to many object-class recognition tasks. Stacking is a common classification approach that combines the results from multiple classifiers, having the added benefit of allowing each classifier to handle a different feature space. However, the standard stacking method by its...
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