Moving object detection is a fundamental task and extensively used research area in modern world computer vision applications. Background subtraction is one of the widely used and the most efficient technique for it, which generates the initial background using different statistical parameters. Due to the enormous size of the video data, the segmentation process requires considerable amount of memory space and time. To reduce the above shortcomings, we propose a statistical background subtraction based motion segmentation method in a compressed transformed domain employing wavelet. We employ the weighted-mean and weighted-variance based background subtraction operations only on the detailed components of the wavelet transformed frame to reduce the computational complexity. Here, weight for each pixel location is computed using pixel-wise median operation between the successive frames. To detect the foreground objects, we employ adaptive threshold, the value of which is selected based on different statistical parameters. Finally, morphological operation, connected component analysis, and flood-fill algorithm are applied to efficiently and accurately detect the foreground objects. Our method is conceived, implemented, and tested on different real video sequences and experimental results show that the performance of our method is reasonably better compared to few other existing approaches.

Moving object detection using statistical background subtraction in wavelet compressed domain

Recognition of both human abnormal activity and daily activity is achieved distinctly with skeleton inputs where geometrical relations of skeletal joint coordinates are observed. These handcrafted features play a significant role to enhance performance. In this paper, the key contribution is that the datasets are reduced to eliminate the complexity of the model by implementing RNN-based approaches. 11,376 skeleton features are reduced to 5 geometrical features selected from 40 different subjects and 12 different action classes of daily and health-related actions. Further, we also introduce an LSTM model to classify medical suspicious activities. The set of relevant geometrical attributes are carefully chosen to train the LSTM model. Experimental work is performed on the multilayer LSTM framework to have a good impact on performance as human activity has a greater diversity. The relations between geometrical different datasets such as selected lines and distances between relevant coordinates of joints are evaluated using NTUD 60 dataset. Also, it is demonstrated that less training data is required for classification between normal and medical activity. The Joint-Joint Distance features show 89.07% accuracy on cross-view evaluation and 78.31% accuracy on cross-subject evaluation.

Suspicious Human Activity Detection Using Pose Estimation and LSTM

Object tracking is the process of locating moving object or multiple objects in sequence of frames. Object tracking is basically a challenging problem. Difficulties in tracking of an object may arise due to abrupt changes in environment, motion of object, noise etc. To overcome such problems different tracking algorithms have been proposed. This paper presents various techniques related to object detection and tracking..The goal of this paper is to present a survey of these techniques.

Detection and Tracking of Moving Object: A Survey

Security Systems plays a very important role in today's modernized industrialized era. Throughout our life, the hard earned assets and valuables things are expected to be safeguarded under certain security features which meet the inquest of the requisite. It is basically designed in order to avoid the risk of vulnerabilities to our valuable items. In this technological world, the system includes biometrics along with digital code lock which response in the way for matching or mismatching the code. Any mismatch to the series of authentication during verification is done raises an alert sound. For biometrical analysis Iris scanner and vein detector is being used which will be monitored with the help of microcontroller through the sensors of the biometric sensors. A keypad will be used for the registered codes such as unique passwords and registered number followed by a wireless motion detector. Any movement occurs to the output of wireless motion detector will be easily sensed by the microcontroller resulting an alert sound. For best assurance, this process of secured authenticity will be active $24\times 7$ that includes at night time as well.

Microcontroller Based Bank Locker Security System Using IRIS Scanner and Vein Scanner

Abstract Textile industries play a major role in the growth of the economy of developing and developed countries. The faulty parts in the fabric are the main problem for the textile industry that majorly affects the quality of the fabric. The faulty parts are difficult to inspect and detect manually. Typically, the textile industry inspects faulty parts in the fabric through a human inspection system that is time-consuming and costly. More importantly, due to human inspection, fewer defects are inspected, which ultimately affects the quality of fabric. Due to this reason, the economic progress of the textile industry is directly affected. Therefore, it is time to develop automatic real-time fabric fault inspection and detection techniques equipped with a computer vision system. The automatic systems greatly improve the accuracy, reliability, and speed compared to the human inspection system. In addition, the automatic inspection and detection system provides a high fault detection rate. The automated system helps reduce labor costs, improves the quality of the product, and increases the efficiency of the manufacturing process. In the proposed work, the discrete wavelet is first realized to inspect the uniformity of fabric using digital fabric images. As the perfect fabric has a steady intervallic structure, the fault in the fabric disrupts the steady formation. Therefore, checking disruption in the fabric thresholding process is also realized. Consequently, the proposed system can detect and precisely locate the defect in the fabric under consideration.

An efficient scheme for the detection of defective parts in fabric images using image processing

Moving object detection is very important in modern world for fast video surveillance. There are various methods used for detecting moving objects out of which frame differencing method is widely used and is most efficient method. In this paper we focus on the surveillance at the most secured areas such as airports, defense establishments, power stations etc. Similarly, the area where no human is allowed without authority to enter such as bank locker rooms, restricted military area etc. automotive surveillance and traffic monitoring plays a vital role. In real time surveillance system, storing the captured video and detecting object are two most important issues. Storing such videos needs more memory and the detection of the object is also need to be fast. To solve these problems compression and fast object detection is required. To detect the moving object, detection of its edges and location in the frame are important steps. In this paper we propose a mechanism to use discrete wavelet transform (DWT) for two purposes for compression and edge detection, whereas to locate the object we propose variance method on to the 2-D DWT outputs of video frames [14]. For this analysis HAAR wavelet is used as reference.

https://ieeexplore.ieee.org/iel7/6872765/6880039/06880123.pdf

Object detection and tracking using 2D — DWT and variance method

Moving object detection is very important in modern world for fast video surveillance. There are various methods used for detecting moving objects out of which frame differencing method is widely used and is most efficient method. In this paper we focus on the surveillance at the most secured areas such as airports, defense establishments, power stations etc. Similarly, the area where no human is allowed without authority to enter such as bank locker rooms, restricted military area etc. automotive surveillance and traffic monitoring plays a vital role. In real time surveillance system, storing the captured video and detecting object are two most important issues. Storing such videos needs more memory and the detection of the object is also need to be fast. To solve these problems compression and fast object detection is required. To detect the moving object, detection of its edges and location in the frame are important steps. In this paper we propose a mechanism to use discrete wavelet transform (DWT) for two purposes for compression and edge detection, whereas to locate the object we propose variance method on to the 2-D DWT outputs of video frames. For this analysis HAAR wavelet is used as reference.

A novel approach based on 2D - DWT and variance method for human detection and tracking in video surveillance applications

In the modern era of the coal fired boilers combustion optimization plays a vital role for increasing the boiler efficiency and reducing the production of hazardous waste gases like NOX, SOX etc. Hence to optimize the combustion there are different ways to control the parameters of boiler combustion like 3T method, which includes Time, Temperature and Turbulence. Each of them can be controlled by controlling variety of parameters available in boiler combustion. One of the methods to control the turbulence of the fire ball is to control firing of the coal and the oil burner's angle and the direction in accordance to get proper tangential firing. In this paper we propose a method based on digital image processing for measurement of furnace oil gun nozzle spray angle measurement using Hough transform.

Application of image processing for spray angle measurement of furnace oil gun nozzle

Moving object detection is fundamental step for automated surveillance system. Generally object detection is performed by some detectors or background subtraction technique. Ultimate aim to detect an object is to detect its edges from the background. There are different methods available for the edge detection of the object. In this paper we propose an idea to detect the edges of the moving object using discrete wavelet transform. 2-D discrete wavelet transform can also be useful for storing of the input video with saving of large amount of memory as compared to the original input video in real time surveillance system. In this method the object detection is based on 2-D discrete wavelet transform error component outputs of the input video frames. For this analysis HAAR wavelet is used as reference.

http://ieeexplore.ieee.org/iel7/6881541/6892507/06892639.pdf

P. P. Gangal

Papers

Object detection and tracking using 2D — DWT and variance method

A novel approach based on 2D - DWT and variance method for human detection and tracking in video surveillance applications

Application of image processing for spray angle measurement of furnace oil gun nozzle

PNew approch for object detection and tracking using 2D - DWT