Softwares
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Convolutional neural networks have achieved state-of-the-art results in tasks such as objection detection and face verification. The development of architectures is a key point to improve the performance of these networks, which are computationally expensive, have a large number of parameters and consume considerable memory. Recent approaches have proposed pruning methods, which consist of finding and removing unimportant filters in these networks. -
The source code associated with the paper “Neural network control for active cameras using master-slave setup“. The package has the code for a learning-based approach to control the master-slave setup and a framework to compare different methods for master-slave camera system. -
SensorCap is an Android tool that captures sensor data in user-defined configurations. The purpose is to allow researchers and developers to quickly save sensor data for research, testing and prototyping. -
This page contains the source code and data used in our paper “Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art". -
Source code of the spatiotemporal feature descriptor proposed in our paper "Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor". -
Smart Surveillance Annotation Tool (SSAT), as it own name indicates, is an annotation tool, free and interactive, for the computer vision community. -
Source code used in the "Kernel Partial Least Squares for Person Re- Identification". In this paper, we approach supervised and unsupervised person re-identification (Re-ID) problem in two widely used datasets using Kernel Partial Least Squares.
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The Smart Sense Laboratory Library (SenseLib) is a C/C++ library built to provide a set of functionalities that aid researchers not only on the development of surveillance systems but also on the creation of novel solutions for problems related to video surveillance. -
Source code used in "Kernel Hierarchical PCA for Person Re-Identification". In this paper, we tackle the person re-identification problem as a common subspace learning and propose a novel framework, Kernel HPCA, that handles with camera transition and dimensionality reduction.