DETECTING GHOST AND LEFT OBJECTS IN SURVEILLANCE VIDEO

2009 ◽  
Vol 23 (07) ◽  
pp. 1503-1525 ◽  
Author(s):  
SIJUN LU ◽  
JIAN ZHANG ◽  
DAVID DAGAN FENG
Keyword(s):  
Object Tracking ◽  
Shape Matching ◽  
Detection Method ◽  
Input Image ◽  
Second Step ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Stationary Objects ◽  
Continuous Object ◽  
Background Image

This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computational power in background modeling and object tracking in video surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of regions in the input image; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by reconstructing the real background using the candidate's corresponding regions in the current input and background image. The effectiveness of our method has been validated by experiments over a variety of video sequences and comparisons with existing state-of-art methods.

Privacy Preserving in Video Surveillance Systems Using Regression Residual Convolutional Neural Network in Private and Public Places

2020 ◽  
Vol 12 (1) ◽  
pp. 39-55
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Security Policy ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Public Places ◽  
Private And Public ◽  
The One ◽  
Regression Residual

In recent years, surveillance video has become a familiar phenomenon because it gives us a feeling of greater security, but we are continuously filmed and our privacy is greatly affected. This work deals with the development of a private video surveillance system (PVSS) using regression residual convolutional neural network (RR-CNN) with the goal to propose a new security policy to ensure the privacy of no-dangerous person and prevent crime. The goal is to best meet the interests of all parties: the one who films and the one who is filmed.

Construction of Shadow Model by Robust Features to Illumination Changes

2013 ◽  
Vol 1 (4) ◽  
pp. 45-55 ◽  
Author(s):  
Shuya Ishida ◽  
Shinji Fukui ◽  
Yuji Iwahori ◽  
M. K. Bhuyan ◽  
Robert J. Woodham
Keyword(s):  
Detection Method ◽  
Harmful Effect ◽  
Color Space ◽  
Shadow Detection ◽  
Edge Information ◽  
Correlation Image ◽  
Illumination Changes ◽  
Sign Correlation ◽  
The Difference ◽  
Background Image

Methods in the field of computer vision need a shadow detection because shadows often have a harmful effect on a result. A new shadow detection method is proposed in this paper. The proposed method is based on the shadow model. The model is constructed by robust features to illumination changes. The proposed method uses the difference of chrominance (UV) components of luma chrominance (YUV) color space between the background image and the observed image, Normalized Vector Distance, Peripheral Increment Sign Correlation image and edge information. These features remove shadow effects in part. The proposed method can construct the effective shadow model by using the features. In addition, the result is improved by the region based method and the shadow model is updated. The proposed method can extract shadows accurately. Results are demonstrated by the experiments using the real videos.

scholarly journals Weighted Kernel Filter Based Anti-Air Object Tracking for Thermal Infrared Systems

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4081
Author(s):  
Chuljoong Kim ◽  
Hanseok Ko
Keyword(s):  
Object Tracking ◽  
High Performance ◽  
Structural Information ◽  
Thermal Infrared ◽  
Visual Object ◽  
Surveillance Systems ◽  
Visual Object Tracking ◽  
Real Time Processing ◽  
Air Surveillance ◽  
Weighted Kernel

Visual object tracking is an important component of surveillance systems and many high-performance methods have been developed. However, these tracking methods tend to be optimized for the Red/Green/Blue (RGB) domain and are thus not suitable for use with the infrared (IR) domain. To overcome this disadvantage, many researchers have constructed datasets for IR analysis, including those developed for The Thermal Infrared Visual Object Tracking (VOT-TIR) challenges. As a consequence, many state-of-the-art trackers for the IR domain have been proposed, but there remains a need for reliable IR-based trackers for anti-air surveillance systems, including the construction of a new IR dataset for this purpose. In this paper, we collect various anti-air thermal-wave IR (TIR) images from an electro-optical surveillance system to create a new dataset. We also present a framework based on an end-to-end convolutional neural network that learns object tracking in the IR domain for anti-air targets such as unmanned aerial vehicles (UAVs) and drones. More specifically, we adopt a Siamese network for feature extraction and three region proposal networks for the classification and regression branches. In the inference phase, the proposed network is formulated as a detection-by-tracking method, and kernel filters for the template branch that are continuously updated for every frame are introduced. The proposed network is able to learn robust structural information for the targets during offline training, and the kernel filters can robustly track the targets, demonstrating enhanced performance. Experimental results from the new IR dataset reveal that the proposed method achieves outstanding performance, with a real-time processing speed of 40 frames per second.

A New Moving Human Detection Method in Color Video Image

2012 ◽  
Vol 229-231 ◽  
pp. 1166-1170
Author(s):  
Tia Nai Wu ◽  
Yun Rong Wu ◽  
Yun Yu Wu
Keyword(s):  
Background Subtraction ◽  
Detection Method ◽  
Main Idea ◽  
Color Difference ◽  
Human Detection ◽  
Background Model ◽  
Video Frame ◽  
Difference Model ◽  
Background Updating ◽  
Background Image

Moving object detection is the basic of video applications such as computer vision, object recognition and tracking, surveillance security etc. Background subtraction and symmetrical differencing are the popular methods of motion detection. The main idea of them is to compare the current video frame with a specified background image or a background model or the next video frame. For background subtraction, the obtaining of initialization is crucial and many methods have been employed, so it is necessary to model background to adapt the changes of background. In this paper, the single gaussian modeling as the initialization background model combined with an improved linear alternate background updating method is proposed. And then, a novel moving human detection method which employs background subtraction and symmetrical differencing based on rgb color difference model is presented. The experimental results show that the detection method can detect moving human effectively and real-time.

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