Head Detection and Tracking for an Intelligent Room

We present a novel feature extraction method, which employs a histogram of transition feature, as an input to a SVM classifier. This feature relies on foreground extraction. We also evaluate some foreground extraction method. To evaluate the performance of this feature, we use it for head detection. Then, by applying a combination of the Harris corner detector and Lucas-Kanade tracker and motion pattern, we track the head position. The performance of the proposed method is experimentally shown.SICE Annual Conference 2014 - International conference on Instrumentation, Control, Information Technology and System Integration, September 9-12, 2014, Hokkaido University, Sapporo, Japa

Ensemble of Different Approaches for a Reliable Person Re-identification System

An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/