Adaptive background reconstruction for street surveillance

In recent years, adaptive background reconstruction works have found interest in many researchers. However, the existing algorithms that have been proposed by other researchers still in the early stage of development and many aspects need to be improved. In this paper, an adaptive background reconstruction is presented. Past pixel observation is used. The proposed algorithm also has eliminated the need of the pre-training of non-moving objects in the background. The proposed algorithm is capable of reconstructing the background with moving objects in video sequence. Experimental results show that the proposed algorithms are able to reconstruct the background correctly and handle illumination and adverse weather that modifies the background

An Adaptive Threshold based FPGA Implementation for Object and Face detection

The moving object and face detection are vital requirement for real time security applications. In this paper, we propose an Adaptive Threshold based FPGA Implementation for Object and Face detection. The input Images and reference Images are preprocessed using Gaussian Filter to smoothen the high frequency components. The 2D-DWT is applied on Gaussian filter outputs and only LL bands are considered for further processing. The modified background with adaptive threshold are used to detect the object with LL band of reference image. The detected object is passed through Gaussian filter to enhance the quality of object. The matching unit is designed to recognize face from standard face database images. It is observed that the performance parameters such as percentage TSR and hardware utilizations are better compared to existing techniques

ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on a histogram of oriented gradients and Markov random field easily captures varying dynamic of the crowded environment.Histogram of oriented gradients along with well-known Markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost.To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation.

Background Subtraction Based on Color and Depth Using Active Sensors

Depth information has been used in computer vision for a wide variety of tasks. Since active range sensors are currently available at low cost, high-quality depth maps can be used as relevant input for many applications. Background subtraction and video segmentation algorithms can be improved by fusing depth and color inputs, which are complementary and allow one to solve many classic color segmentation issues. In this paper, we describe one fusion method to combine color and depth based on an advanced color-based algorithm. This technique has been evaluated by means of a complete dataset recorded with Microsoft Kinect, which enables comparison with the original method. The proposed method outperforms the others in almost every test, showing more robustness to illumination changes, shadows, reflections and camouflage.This work was supported by the projects of excellence from Junta de Andalucia MULTIVISION (TIC-3873), ITREBA (TIC-5060) and VITVIR (P11-TIC-8120), the national project, ARC-VISION (TEC2010-15396), and the EU Project, TOMSY (FP7-270436)

FPGA implementation of moving object and face detection using adaptive threshold

The real time moving object and face detections are used for various security applications. In this paper, we propose FPGA implementation of moving object and face detection with adaptive threshold. The input images are passed through Gaussian filter. The 2D-DWT is applied on Gaussian filter output and considered only LL band for further processing to detect object/face. The modified background subtraction technique is applied on LL bands of input images. The adaptive threshold is computed using LL-band of reference image and object is detected through modified background subtraction. The detected object is passed through Gaussian filter to get final good quality object. The face detection is also identified using matching unit along with object detection unit. The reference image is replaced by face database images in the face detection. It is observed that the performance parameters such as TSR, FRR, FAR and hardware related results are improved compared to existing techniques

FPGA IMPLEMENTATION OF MOVING OBJECT AND FACE DETECTION USING ADAPTIVE THRESHOLD

The real time moving object and face detections are used for various security applications. In this paper, we propose FPGA implementation of moving object and face detection with adaptive threshold. The input images are passed through Gaussian filter. The 2D-DWT is applied on Gaussian filter output and considered only LL band for further processing to detect object/face. The modified background subtraction technique is applied on LL bands of input images. The adaptive threshold is computed using LL-band of reference image and object is detected through modified background subtraction. The detected object is passed through Gaussian filter to get final good quality object. The face detection is also identified using matching unit along with object detection unit. The reference image is replaced by face database images in the face detection. It is observed that the performance parameters such as TSR, FRR, FAR and hardware related results are improved compared to existing techniques

Background Subtraction in Video Surveillance

The aim of thesis is the real-time detection of moving and unconstrained surveillance environments monitored with static cameras. This is achieved based on the results provided by background subtraction. For this task, Gaussian Mixture Models (GMMs) and Kernel density estimation (KDE) are used. A thorough review of state-of-the-art formulations for the use of GMMs and KDE in the task of background subtraction reveals some further development opportunities, which are tackled in a novel GMM-based approach incorporating a variance controlling scheme. The proposed approach method is for parametric and non-parametric and gives us the better method for background subtraction, with more accuracy and easier parametrization of the models, for different environments. It also converges to more accurate models of the scenes. The detection of moving objects is achieved by using the results of background subtraction. For the detection of new static objects, two background models, learning at different rates, are used. This allows for a multi-class pixel classification, which follows the temporality of the changes detected by means of background subtraction. In a first approach, the subtraction of background models is done for parametric model and their results are shown. The second approach is for non-parametric models, where background subtraction is done using KDE non-parametric model. Furthermore, we have done some video engineering, where the background subtraction algorithm was employed so that, the background from one video and the foreground from another video are merged to form a new video. By doing this way, we can also do more complex video engineering with multiple videos. Finally, the results provided by region analysis can be used to improve the quality of the background models, therefore, considerably improving the detection results