Person re-identification using robust brightness transfer functions based on multiple detections

Re-identification systems aim at recognizing the same individuals in multiple cameras and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of Minimum Multiple Cumulative Brightness Transfer Functions to model this appearance variations. It is multiple frame-based learning approach which leverages consecutive detections of each individual to transfer the appearance, rather than learning brightness transfer function from pairs of images.We tested our approach on standard multi-camera surveillance datasets showing consistent and significant improvements over existing methods on two different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method. \ua9 Springer International Publishing Switzerland 201

Supervised learning for Human Action Recognition from multiple Kinects

International audienceThe research of Human Action Recognition (HAR) has made a lot of progress in recent years, and the research based on RGB images is the most extensive. However , there are two main shortcomings: the recognition accuracy is insufficient, and the time consumption of the algorithm is too large. In order to improve these issues our project attempts to optimize the algorithm based on the random forest algorithm by extracting the features of the human body 3D, trying to obtain more accurate human behavior recognition results, and can calculate the prediction results at a lower time cost. In this study, we used the 3D spatial coordinate data of multiple Kinect sensors to overcome these problems and make full use of each data feature. Then, we use the data obtained from multiple Kinects to get more accurate recognition results through post processing

A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches

Physically falling down amongst the elder helpless party is one of the most intractable issues in the era of ageing society, which has attracted intensive attentions in academia ranging from clinical research to computer vision studies. This paper proposes a fall detection/recognition system within the realm of computer vision. The proposed system integrates a group of gradient-based local visual feature extraction approaches, including histogram of oriented gradients (HOG), histogram of motion gradients (HMG), histogram of optical flow (HOF), and motion boundary histograms (MBH). A comparative study of the descriptors with the support of an artificial neural network was conducted based on an in-house captured dataset. The experimental results demonstrated the effectiveness of the proposed system and the power of these descriptors in real-world applications