Using the discrete hadamard transform to detect moving objects in surveillance video

In this paper we present an approach to object detection in surveillance video based on detecting moving edges using the Hadamard transform. The proposed method is characterized by robustness to illumination changes and ghosting effects and provides high speed detection, making it particularly suitable for surveillance applications. In addition to presenting an approach to moving edge detection using the Hadamard transform, we introduce two measures to track edge history, Pixel Bit Mask Difference (PBMD) and History Update Value (H UV ) that help reduce the false detections commonly experienced by approaches based on moving edges. Experimental results show that the proposed algorithm overcomes the traditional drawbacks of frame differencing and outperforms existing edge-based approaches in terms of both detection results and computational complexity

A search for Earth-crossing asteroids, supplement

The ground based electro-optical deep space surveillance program involves a network of computer controlled 40 inch 1m telescopes equipped with large format, low light level, television cameras of the intensified silicon diode array type which is to replace the Baker-Nunn photographic camera system for artificial satellite tracking. A prototype observatory was constructed where distant artificial satellites are discriminated from stars in real time on the basis of the satellites' proper motion. Hardware was modified and the technique was used to observe and search for minor planets. Asteroids are now routinely observed and searched. The complete observing cycle, including the 2"-3" measurement of position, requires about four minutes at present. The commonality of asteroids and artificial satellite observing, searching, data reduction, and orbital analysis is stressed. Improvements to the hardware and software as well as operational techniques are considered

Statistical approach for detection of vehicle in heavy traffic

In this thesis an innovative system for detecting and extracting vehicles in traffic surveillance scenes is presented .The main concept behind vehicle detection in a live video is extract the foreground and remove the background from it .This theory is called background subtraction .This method can be implemented in various ways such as setting a particular threshold value and removing the objects having value less than it .The second approach is to compare the current frame with the previous frame and if the variance is more than a certain value it detects the motion of that object .Third and the most efficient method is to use a statistical method where a certain number of video frames are used to initialize a fixed number of Gaussian modes in the mixture model. While in the first method only white cars are being detected this disadvantage is solved when we use a statistical method where a particular vehicle is detected using a foreground detection technique on a frame .Here the input video file is read in AVI format .After that morphological operations are done on it and the bounding box is calculated .Finally the moving object is presented with a rectangle drawn around it and total number of vehicles in the current frame is calculated .This process is repeated for each frame till the whole video is processed .Since this method uses a training set and not a general threshold selected manually by the user the foreground extracted is more desirable than other method and besides it requires much less memory than the method where the background subtraction is done by comparing the frame with the previous one .And last but not the least it gives a general idea about the vehicle frequency in the video which can be very helpful in traffic monitoring

CVABS: Moving Object Segmentation with Common Vector Approach for Videos

Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring. An accurate background model helps detecting activity of moving objects in the video. In this work, we have developed a new subspace based background modelling algorithm using the concept of Common Vector Approach with Gram-Schmidt orthogonalization. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilized as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl

Computer supported estimation of input data for transportation models

Control and management of transportation systems frequently rely on optimization or simulation methods based on a suitable model. Such a model uses optimization or simulation procedures and correct input data. The input data define transportation infrastructure and transportation flows. Data acquisition is a costly process and so an efficient approach is highly desirable. The infrastructure can be recognized from drawn maps using segmentation, thinning and vectorization. The accurate definition of network topology and nodes position is the crucial part of the process. Transportation flows can be analyzed as vehicle’s behavior based on video sequences of typical traffic situations. Resulting information consists of vehicle position, actual speed and acceleration along the road section. Data for individual vehicles are statistically processed and standard vehicle characteristics can be recommended for vehicle generator in simulation models

Adaptive threshold for moving objects detection using gaussian mixture model

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection