A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. In video surveillance based face recognition, face images are typically captured over multiple frames in uncontrolled conditions; where head pose, illumination, shadowing, motion blur and focus change over the sequence. We can generalize that the three fundamental operations involved in the facial recognition tasks: face detection, face alignment and face recognition. This study presents comparative benchmark tables for the state-of-art face recognition methods by testing them with same backbone architecture in order to focus only on the face recognition solution instead of network architecture. For this purpose, we constructed a video surveillance dataset of face IDs that has high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses

Evaluation of video based pedestrian and vehicle detection algorithms

Video based detection systems rely on the ability to detect moving objects in video streams. Video based detection systems have applications in many fields like, intelligent transportation, automated surveillance etc. There are many approaches adopted for video based detection. Evaluation and selecting a suitable approach for pedestrian and vehicle detection is a challenging task. While evaluating the object detection algorithms, many factors should be considered in order to cope with unconstrained environments, non stationary background, different object motion patterns and the variation in types of object being detected. In this thesis, we implement and evaluate different video based detection algorithms used for pedestrian and vehicle detection. Video based pedestrian and vehicle detection involves object detection through background foreground segmentation and object tracking. For background foreground segmentation, frame differencing, background averaging, mixture of Gaussians and codebook methods were implemented. For object tracking, Mean-Shift tracking and Lucas Kanade optical flow tracking algorithms were implemented. The performance of each of these algorithms is evaluated by a comparative study; based on their performance such as ability to get good detection and tracking, CodeBook algorithm is selected as a candidate algorithm for background foreground segmentation and Mean-Shift tracking is used to track the detected objects for pedestrian and vehicle detection

Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/