Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

A calibration method for non-overlapping cameras based on mirrored phase target

A novel calibration method for non-overlapping cameras is proposed in this paper. A LCD screen is used as a phase target to display two groups of orthogonal phase-shifted sinusoidal patterns during the calibration process. Through a mirror reflection, the phase target is captured by the cameras respectively. The relations between each camera and the phase target can be obtained according the proposed algorithm. Then the relation between the cameras can be calculated by treating the phase target as an intermediate value. The proposed method is more flexible than conventional mirror-based approach, because it do not require the common identification points and is robust to out-of-focus images. Both simulation work and experimental results show the proposed calibration method has a good result in calibrating a non-overlapping cameras system

Matching of objects moving across disjoint cameras

Matching of single individuals as they move across disjoint camera views is a challenging task in video surveillance. In this paper, we present a novel algorithm capable of matching single individuals in such a scenario based on appearance features. In order to reduce the variable illumination effects in a typical disjoint camera environment, a cumulative color histogram transformation is first applied to the segmented moving object. Then, an incremental major color spectrum histogram representation (IMCSHR) is used to represent the appearance of a moving object and cope with small pose changes occurring along the track. An IMCHSR-based similarity measurement algorithm is also proposed to measure the similarity of any two segmented moving objects. A final step of post-matching integration along the object's track is eventually applied. Experimental results show that the proposed approach proved capable of providing correct matching in typical situations. ©2006 IEEE

A calibration method for non-overlapping cameras based on mirrored phase target

A novel calibration method for non-overlapping cameras is proposed in this paper. A LCD screen is used as a phase target to display two groups of orthogonal phase-shifted sinusoidal patterns during the calibration process. Through a mirror reflection, the phase target is captured by the cameras respectively. The relations between each camera and the phase target can be obtained according the proposed algorithm. Then the relation between the cameras can be calculated by treating the phase target as an intermediate value. The proposed method is more flexible than conventional mirror-based approach, because it do not require the common identification points and is robust to out-of-focus images. Both simulation work and experimental results show the proposed calibration method has a good result in calibrating a non-overlapping cameras system

Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms

Tracking people across disjoint camera views by an illumination-tolerant appearance representation

Tracking single individuals as they move across disjoint camera views is a challenging task since their appearance may vary significantly between views. Major changes in appearance are due to different and varying illumination conditions and the deformable geometry of people. These effects are hard to estimate and take into account in real-life applications. Thus, in this paper we propose an illumination-tolerant appearance representation, which is capable of coping with the typical illumination changes occurring in surveillance scenarios. The appearance representation is based on an online k-means colour clustering algorithm, a data-adaptive intensity transformation and the incremental use of frames. A similarity measurement is also introduced to compare the appearance representations of any two arbitrary individuals. Post-matching integration of the matching decision along the individuals' tracks is performed in order to improve reliability and robustness of matching. Once matching is provided for any two views of a single individual, its tracking across disjoint cameras derives straightforwardly. Experimental results presented in this paper from a real surveillance camera network show the effectiveness of the proposed method. © Springer-Verlag 2007