Foreground-Background Segmentation Based on Codebook and Edge Detector

During past few years’ significant usage of video monitoring has been observed in several places. Video monitoring has always helped in improvement of security specific aspects. These systems provide major helping hand in monitoring through multiple object detection methods. However, there are certain challenges related to weather conditions which needs to be addressed. In view of this in this paper, we study multiple object detection in surveillance videos through hybrid deep gaussian mixture ensembles. This method integrates multiple deep gaussian mixture components to form ensembles which helps to develop multiple object detection solutions in surveillance videos. The computational system consists of ensemble of three major deep gaussian mixture components. The ensemble pipeline is formed with augmenting, bagging and stacking methods. The ensemble consists of hybrid object detection paradigm with steps background analysis, video pre-processing, data integration, object detection, foreground detection and post-processing. The computational pipeline is successfully experimented with ViSOR and CDnet 2014 benchmarked datasets. All results are validated with accuracy, precision, sensitivity or recall, specificity, F1-Score and RMSE metrics. Several comparative studies are performed with state-of-the-art methods as well as baselines. The experimental results demonstrate superiority of this method in comparison with other methods. Ablation studies have also been performed here with superior results. This system presents robustness in multiple object detection for real life challenges such as sudden illumination variation, shadow presence, long term occlusion and formation of dynamic backgrounds. It provides cost-effective, more profitable, efficient and sustainable real time multiple object detection solutions

Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.

Moving objects detection in video is essential for many computer vision applications and it is considered as a challenging research issue due to dynamic changes in object size, shape, complex background and illumination changes. In this research article a novel method to detect moving objects in video is proposed grounded on Locality Preserving Projections (LPP) and also thorough analysis of variations in neighbor mode and weight mode for constructing adjacency graph is given. LPP is an unsupervised subspace learning approach used for dimensionality reduction, which preserves the neighborhood structure of dataset while detection, which is vital in further steps accurate tracking and recognition of objects. The proposed method is tested on standard datasets with complex environments and experimental results obtained with variations in neighborhood modes and weight modes are encouraging.

Object detection in videos is widely used in entertainment, robotics, surveillance etc. A major challenge in effective object detection is observed when there is occlusion, bad illumination or cluttered background. Besides, cameras do not provide any mechanism for detecting moving objects after capture. Researchers have proposed different methods for object detection in video frames, ranging from traditional to deep learning approaches. However, using the right method in the right situation for efficient and accurate detection is a concern. Even though deep learning has shown high accuracy in detection, the training and testing time and cost is a concern. In our research, we explore the best motion models for tracking objects in a video sequence. The characteristics of each model was analyzed and the results were compared using different types of videos. Our observations determine that depending on the quality of input video, traditional approaches show high accuracy in detection, comparable to state-of-the art methods.

This work proposes a novel method for detection of motion based object having dynamic scenario in the background. The suggested scheme has a strong potential for real-time applications especially for rafting, river, sea-beach, swimming pools, ponds, etc. Apart from these, this work is very beneficial for surveillance of border, tunnel, traffic in the sea, forest, restricted zones, deep zones, etc. This work develops a statistical p based background subtraction method and implemented in three stages. In the first stage, a background model is developed using few initial frames. In the second stage, this work classifies the foreground using the difference frame and the appropriate threshold value. An automatic threshold value is generated at run-time and updated iteratively. It also reduces the problem of using a constant threshold. In the third stage, morphological filters and connected component based region filtering technique is applied to enhance the detection quality. The extensive experimental result shows more accurate results of proposed method. It also demonstrates better performance against considered state-of-the-art methods.

Moving Object Detection systems have long been in use to look at security sensitive areas. The manufacture of video investigating systems sensible desires fast, reliable and durable algorithms for moving object detection classification pursuit and activity analysis. Moving object detection is that the essential step for extra analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes like human, vehicle, animal, clutter, etc. it's a necessity to inform apart objects from each other therefore on trace and analyse their actions reliably. In previous system we have got performed background subtraction by victimization clever Edge Detection. In clever Edge Detection technique we tend to tend to square measure taking 2 photos for comparison those square measure background image and foreground image. Previous strategies for object detection square measure methodology along side object detectors image segmentation and Background subtraction. Our methodology aims to section objects supported motion info and it contains a part of background modelling. In the previous strategies we tend to tend to square measure conducting background subtraction only for photos. For this we tend to tend to projected an image component wise background modelling and subtraction technique mistreatment multiple choices. Hence, throughout this color, gradient and Hear-Like choices square measure integrated to handle the variation element. Thus, motion segmentation and background subtraction square measure the foremost connected topics to the current paper.

Video motion target tracking has been widely used in the field of sports. Content-based video search has a broad application prospect and attracts more and more researchers’ attention. In view of the shortcomings of moving object detection in motion video at present, a human motion recognition method based on correlation vector machine is proposed. Combined with the improved Gaussian mixture model of moving object detection and tracking method in motion video, moving object detection and tracking are carried out. The design of the system is realized. Firstly, it collects sports videos, considers all kinds of sports objects in sports videos, uses improved background update difference method to detect sports objects, then uses correlation vector machine to recognize human motion, and finally uses AdaBoost classifier to use it in multiple sports videos and sports objects. Carry out follow-up experiments. Results in this way, we can track the sports target in sports video accurately and quickly, and the real-time performance of sports target tracking is better than other tracking methods, which provides a new research tool for tracking sports target in sports video.

Detection of video objects under bad weather and poor illumination condition is a challenging task. We address this issue using the notion of background modeling. LBP-based background modeling and model learning has been used to detect the video objects but performance degrades in the above complex background. We propose the adjacency- and reinforced adjacency-based variants of LBP for complex real-world background modeling and model learning for object detection. In this regard, we have proposed the four variants; (1) enhanced adjacent local binary pattern, (2) enhanced reinforced adjacent local binary pattern, (3) enhanced adjacent local ternary pattern, and (4) enhanced reinforced adjacent local ternary pattern. Besides, we have embedded the Gabor and LBP features to obtain an embedded feature, which is subsequently used with the notions of adjacency. Unlike the background learning approach where the model learns only the background, our model learning algorithm learns the background together with the foreground objects and hence named as mixed-mode learning strategy. These models together with the new learning strategy are tested with CD2014 (snowfall and blizzard) and PETS (2014 and 2016) data sets, and the performance of the proposed models has been compared with LBP-based methods and other state-of-the-art methods.

Approximation-free running SVD and its application to motion detection

Today, the rapid development of Artificial Intelligence technologies is effective in the success of deep learning algorithms in different application areas. In addition to these applications, it detects many objects that cannot be noticed even with the human eye in object detection in video and images with deep learning algorithms. In this study, it was aimed to detect weapons by using images obtained from UAV using deep learning algorithms. Regional Based Convolutional Neural Networks and Residual Network were used and the performance evaluation of the architecture of these algorithms was presented. Performance evaluation of algorithms was made using Loss plots, Precision-Recall, and mAP curves. In this study, 200 images of different angles and heights were obtained from the unmanned aerial vehicle. Two-thirds of the images we obtained were divided into training and one-third test images. Resnet and Region Based Convolutional neural networks used in test images have been successful in object detection. Regional based neural networks and Residual Network that we use for object detection are used. Images from different angles and heights obtained from the unmanned aerial vehicle are trained using regional-based neural networks and residual network algorithms. The feature maps extracted from the images used for training were compared with the test images. By bringing these compared images closer to the desired images, the desired image detection was detected at a rate of 99%. The performance evaluation of the detected images is discussed and the success of the deep learning algorithms used in object detection is presented. Considered in the performance evaluation of deep learning algorithms, Loss Charts (Error), Precision-Recall curves show the accuracy of detection in a short time. Algorithms that will increase the possibilities and capabilities of unmanned aerial vehicles used especially in border security and internal security are considered to increase in the future.

Salient object detection in video has attracted enormous research efforts for its wide applicability. But there are still some issues in restraining the disturbance of background, which make it difficult to detect salient object in complex scenarios. Inspired by the hypothesis of center prior in image domain, we novelly introduced the concept of dynamic attention center in video. The distance between specific regions and this center is used as a weighting term to restrain the influence of background disturbance and obtain more accurate spatial and temporal saliency maps. Besides, we develop a dynamic fusion method to combine the temporal and spatial saliency map, leading to higher spatiotemporal consistency. The experiments on Freiburg-Berkeley Motion Segmentation Dataset show that our method outperforms several state-of-art methods on subjective visual perception and objective measurements.

Soft Computing based object detection and tracking approaches: State-of-the-Art survey

Despite recent breakthroughs in object detection with static images, extending state-of-the-art object detectors from image to video is challenging. The detection accuracy suffers from degenerated object appearances in videos, e.g., occlusion, video defocus, motion blur, etc. In this paper, we present a new framework called Missing Recover Recurrent Neural Networks (MR-RNN) for improving object detection in videos, which captures temporal information to recover the missing object. First, We detect objects in consecutive frames to obtain the bounding boxes and their confidence scores. The detector is trained for every frame of the video. Then we feed these detections into a Recurrent Neural Network (LSTM [8] or BiLSTM [4]) to capture temporal information. This method is tested on a large-scale vehicle dataset, “DETRAC”. Our approach achieves Average Precision (AP) of 68.90 based on SSD detector, an improvement of 2.68 over the SSD detector. Experimental results show that our method successfully detects many objects which are missed by basic detectors.

Deep learning models are widely used in object detection area, including combination of multiple non-linear data transformations. The objective is receiving brief and concise information for feature representations. Due to the high volume of processing data, object detection in videos has been faced with big challenges, such as mass calculation. To increase the object detection precision in videos, a hybrid method is proposed, in this paper. Some modifications are applied to auto encoder neural networks, for the compact and discriminative learning of object features. Furthermore, for object classification, firstly extracted features are transferred to a convolutional neural network, and after feature convolution with input pictures, they will be classified. The proposed method has two main advantages over other unsupervised feature learning techniques. Firstly, as it will be shown, features are detected with a much higher precision. Secondly, in the proposed method, the outcome is compact and additional unnecessary information is removed; while the existing unsupervised feature learning models mainly learn repeated and redundant information of the features. Experimental evaluation shows that precision of feature detection improved by 1.5% in average in compare with the state-of-the-art methods.

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