Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Abstract

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals

“…Nowadays, intelligent computing and machine learning are the primary underlying technologies employed to develop automated algorithms and design learning strategies for finding the optimal solution to a large variety of tasks. [57][58][59][60][61] The state-of-the-art validates the incredible power of generative adversarial learning for image synthesis starting from text, sketch, or another image, as the information source. However, nowadays, the Wi-Fi signal is being explored for synthesizing visual data, opening up a new frontier for image synthesis and surveillance applications.…”

Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application

“…In the last decade, machine learning and deep learning algorithms have become widespread tools to address many computer vision-based problems, including medical imaging analysis [12], person re-identification [11,7], environment monitoring [15,14,8], emotion recognition [10], handwriting validation [3,4], background modeling [2], and video synthesis [5]. Although effective, these methods usually require a large amount of training data which, however, is not always available.…”

MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

“…In recent years, computer vision has been widely used in several heterogeneous tasks, including deception detection [1][2][3], background modeling [4][5][6], and person reidentification [7][8][9]. In addition, thanks to technological advancement, it is possible to execute computer-vision algorithms on different devices, including robots [10,11] and drones [12,13].…”