Person Re-Identification Techniques for Intelligent Video Surveillance Systems

Nowadays, intelligent video-surveillance is one of the most active research fields in com- puter vision and machine learning techniques which provides useful tools for surveillance operators and forensic video investigators. Person re-identification is among these tools; it consists of recognizing whether an individual has already been observed over a network of cameras. This tool can also be employed in various possible applications, e.g., off-line retrieval of all the video-sequences showing an individual of interest whose image is given as query, or on-line pedestrian tracking over multiple cameras. For the off-line retrieval applications, one of the goals of person re-identification systems is to support video surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously ob- served individuals for decreasing values of their similarity with a given probe individual. This task is typically achieved by exploiting the clothing appearance, in which a classical biometric methods like the face recognition is impeded to be practical in real-world video surveillance scenarios, because of low-quality of acquired images. Existing clothing appearance descriptors, together with their similarity measures, are mostly aimed at im- proving ranking quality. These methods usually are employed as part-based body model in order to extract image signature that might be independently treated in different body parts (e.g. torso and legs). Whereas, it is a must that a re-identification model to be robust and discriminate on individual of interest recognition, the issue of the processing time might also be crucial in terms of tackling this task in real-world scenarios. This issue can be also seen from two different point of views such as processing time to construct a model (aka descriptor generation); which usually can be done off-line, and processing time to find the correct individual from bunch of acquired video frames (aka descriptor matching); which is the real-time procedure of the re-identification systems. This thesis addresses the issue of processing time for descriptor matching, instead of im- proving ranking quality, which is also relevant in practical applications involving interaction with human operators. It will be shown how a trade-off between processing time and rank- ing quality, for any given descriptor, can be achieved through a multi-stage ranking approach inspired by multi-stage approaches to classification problems presented in pattern recogni- tion area, which it is further adapting to the re-identification task as a ranking problem. A discussion of design criteria is therefore presented as so-called multi-stage re-identification systems, and evaluation of the proposed approach carry out on three benchmark data sets, using four state-of-the-art descriptors. Additionally, by concerning to the issue of processing time, typical dimensional reduction methods are studied in terms of reducing the processing time of a descriptor where a high-dimensional feature space is generated by a specific person re-identification descriptor. An empirically experimental result is also presented in this case, and three well-known feature reduction methods are applied them on two state-of-the-art descriptors on two benchmark data sets

AUTO LANDING PROCESS FOR AUTONOMOUS FLYING ROBOT BY USING IMAGE PROCESSING BASED ON EDGE DETECTION

In today’s technological life, everyone is quite familiar with the importance of security measures in our lives. So in this regard, many attempts have been made by researchers and one of them is flying robots technology. One well-known usage of flying robot, perhaps, is its capability in security and care measurements which made this device extremely practical, not only for its unmanned movement, but also for the unique manoeuvre during flight over the arbitrary areas. In this research, the automatic landing of a flying robot is discussed. The system is based on the frequent interruptions that is sent from main microcontroller to camera module in order to take images; these images have been distinguished by image processing system based on edge detection, after analysing the image the system can tell whether or not to land on the ground. This method shows better performance in terms of precision as well as experimentally

A Multi-Stage Ranking Approach for Fast Person Re-Identification

One of the goals of person re-identification systems is to support video-surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously observed individuals for decreasing values of their similarity with a given probe individual. Existing appearance descriptors, together with their similarity measures, are mostly aimed at improving ranking quality. We address instead the issue of processing time, which is also relevant in practical applications involving interaction with human operators. We show how a trade-off between processing time and ranking quality, \emph{for any given descriptor}, can be achieved through a multi-stage ranking approach inspired by multi-stage classification approaches, which we adapt to the re-identification ranking task. We analytically model the processing time of multi-stage system and discuss the corresponding accuracy, and derive from these results practical design guidelines. We then emprically evaluate our approach on three benchmark data sets and four state-of-the-art descriptors

Using diversity for classifier ensemble pruning: an empirical investigation

The concept of “diversity” has been one of the main open issues in the field of multiple classifier systems. In this paper we address a facet of diversity related to its effectiveness for ensemble construction, namely, explicitly using diversity measures for ensemble construction techniques based on the kind of overproduce and choose strategy known as ensemble pruning. Such a strategy consists of selecting the (hopefully) more accurate subset of classifiers out of an original, larger ensemble. Whereas several existing pruning methods use some combination of individual classifiers’ accuracy and diversity, it is still unclear whether such an evaluation function is better than the bare estimate of ensemble accuracy. We empirically investigate this issue by comparing two evaluation functions in the context of ensemble pruning: the estimate of ensemble accuracy, and its linear combination with several well-known diversity measures. This can also be viewed as using diversity as a regularizer, as suggested by some authors. To this aim we use a pruning method based on forward selection, since it allows a direct comparison between different evaluation functions. Experiments on thirty-seven benchmark data sets, four diversity measures and three base classifiers provide evidence that using diversity measures for ensemble pruning can be advantageous over using only ensemble accuracy, and that diversity measures can act as regularizers in this context