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.
Highlights
Last years have seen the design of increasingly advanced computer vision algorithms to support a wide range of critical tasks in a plethora of application areas
Concerning the datasets, the experiments were performed on iLIDS-VID [38], Person Re-ID (PRID) 2011 [39], and Motion Analysis and Re-identification Set (MARS) [40]
The iLIDS-VID dataset is comprised of 600 image sequences belonging to 300 people acquired by two non-overlapping cameras
Summary
Last years have seen the design of increasingly advanced computer vision algorithms to support a wide range of critical tasks in a plethora of application areas. These algorithms often have the responsibility of taking determining decisions in issues where failure would lead to serious consequences. In References [1,2,3], for example, vision-based systems are used for inspection of pipeline infrastructures. In the first work, the authors presented a method for subsea pipeline corrosion estimation by using colour information of corroded pipes. In the second and third work, the authors focused on large infrastructures, Sensors 2020, 20, 5365; doi:10.3390/s20185365 www.mdpi.com/journal/sensors
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