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Found 6 papers, 2 papers with code
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for example, by issuing warnings to the driver or acting actively on car's brakes, it helps decreasing the probability of injuries and human fatalities.
First Steps Toward Camera Model Identification with Convolutional Neural Networks
Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution.
Deep convolutional neural networks for pedestrian detection
Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics.
Fast keypoint detection in video sequences
A number of computer vision tasks exploit a succinct representation of the visual content in the form of sets of local features.
Hybrid coding of visual content and local image features
Traditionally, a Compress-Then-Analyze approach has been pursued, in which sensing nodes acquire and encode the pixel-level representation of the visual content, that is subsequently transmitted to a sink node in order to be processed.
Coding local and global binary visual features extracted from video sequences
In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding.
