Exploiting Bounded Sensor Field-of-View Geometry in Tracking and Sensor Planning Problems

In search-detect-track problems, knowledge of where objects were not seen can be as valuable as knowledge of where objects were seen. Exploiting the sensor's known sensing extents, or field-of-view (FoV), this type of evidence can be incorporated in a Bayesian framework to improve tracking accuracy and form better sensor schedules. This paper presents new techniques for incorporating bounded FoV inclusion/exclusion evidence in object state densities and multi-object cardinality distributions. Some examples of how the proposed techniques may be applied to tracking and sensor planning problems are given

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/