6 Result(s)
within
Rao Muhammad Anwer
Artificial Intelligence
Image Processing and Computer Vision
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Chapter and Conference Paper
Count- and Similarity-Aware R-CNN for Pedestrian Detection
Recent pedestrian detection methods generally rely on additional supervision, such as visible bounding-box annotations, to handle heavy occlusions. We propose an approach that leverages pedestrian count and pr...
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Chapter and Conference Paper
Multi-stream Convolutional Networks for Indoor Scene Recognition
Convolutional neural networks (CNNs) have recently achieved outstanding results for various vision tasks, including indoor scene understanding. The de facto practice employed by state-of-the-art indoor scene r...
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Chapter and Conference Paper
Top-Down Deep Appearance Attention for Action Recognition
Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, w...
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Chapter and Conference Paper
Deep Semantic Pyramids for Human Attributes and Action Recognition
Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to prov...
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Article
Coloring Action Recognition in Still Images
In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e....
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Chapter and Conference Paper
Opponent Colors for Human Detection
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding ...
