In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Moving object detection is the basis of object tracking and classification in computer vision, which has been applied to underwater robots to execute underwater missions and do marine ecological research. The complicated scene and poor lighting condition in underwater environment usually make moving object detection difficult. To solve above problems and to detect moving object from underwater video, we propose an approach combining background subtraction and three-frame difference. In this method, firstly we detect moving object pixels by background subtraction and three-frame difference perspectively. Next, we perform "AND" operation on the results of background subtraction and three-frame difference, background subtraction provides the object information to supplement the incomplete information detected from three-frame difference. Lastly, morphology processing is utilized on the result to remove the noise caused by non-static objects in background. This method shows an effective and reliable performance in detecting moving object from underwater video.

Combining background subtraction and three-frame difference to detect moving object from underwater video

People-flow counting is one of the key techniques of intelligence video surveillance systems and the information of people-flow obtained from this technique is an very important evidence for many applications, such as business analysis, staff planning, security, etc. Traditionally, the color image information based methods encounter kinds of challenges, such as shadows, illumination changing, cloth color, etc., while the depth information based methods suffer from lack of texture. In this paper, we propose an effective approach of people-flow counting by combining color and depth information. First, we adopt a background subtraction technique to fast obtain the moving regions on depth images. Second, the water filling algorithm is used to effectively detect head candidates on the moving regions. Then we use the SVM to recognize the real heads from the candidates. Finally, we adopt a weighted K Nearest Neighbor based multi-target tracking method to track each confirmed head and count the people through the surveillance region. Four datasets constructed from two surveillance scenes are used to evaluate the proposed method. Experimental results show that our method outperform the state-of-the-art methods. Our method can work stably on condition of kinds of interruptions and can not only obtain high precisions, but also high recalls on four datasets.

People-flow counting in complex environments by combining depth and color information

Moving object detection (MOD) is a significant problem in computer vision that has many real world applications. Different categories of methods have been proposed to solve MOD. One of the challenges is to separate moving objects from illumination changes and shadows that are present in most real world videos. State-of-the-art methods that can handle illumination changes and shadows work in a batch mode; thus, these methods are not suitable for long video sequences or real-time applications. In this paper, we propose an extension of a state-of-the-art batch MOD method (ILISD) to an online/incremental MOD using unsupervised and generative neural networks, which use illumination invariant image representations. For each image in a sequence, we use a low-dimensional representation of a background image by a neural network and then based on the illumination invariant representation, decompose the foreground image into: illumination change and moving objects. Optimization is performed by stochastic gradient descent in an end-to-end and unsupervised fashion. Our algorithm can work in both batch and online modes. In the batch mode, like other batch methods, optimizer uses all the images. In online mode, images can be incrementally fed into the optimizer. Based on our experimental evaluation on benchmark image sequences, both the online and the batch modes of our algorithm achieve state-of-the-art accuracy on most data sets.

pdf/online-illumination-invariant-moving-object-detection-by-3vrq8l7c1r.pdf

Online Illumination Invariant Moving Object Detection by Generative Neural Network

All video surveillance system uses some important steps to detach moving object from background and thus differentiate all the objects in video image frames. The main endeavor of this paper is to use an object bounding box concept to detect the human blob and human activity detection using a system. The images are captured by using single static camera to make the system cost effective. The bounding box that bounds each object in the image frames can be utilized to track and detect activities of the moving objects in further frames. These bounding boxes can be utilized to detect human and human activities like crowd and the estimation of crowd. This paper gives the implementation results of bounding box for activity detection.

Design and Utilization of Bounding Box in Human Detection and Activity Identification

Moving object detection in video streams is the fundamental and relevant step in many computer vision applications such as video surveillance for people tracking. Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. This paper describes an efficient background subtraction technique for extracting the moving objects from a scene. Gaussian mixture models (GMM) gives best results than other segmentation methods.

An efficient low cost background subtraction method to extract foreground object during human tracking

Nowadays video surveillance system becomes popular to ensure security at major areas. Current video surveillance system analyzes the tracking of multiple human in a video. The problem of detecting moving targets in crowded scenes is one of the most challenging topics in such systems. Occlusion is said to be one object hides another during multiple human tracking in a series of frames or in one frame. Occlusion detection and handling is an important phase for such systems. The main objective of the proposed work is to detect occlusion. In the proposed work, patches in each object are used to identify the parts which are going to be occluded. This system can identify the occlusion between humans in a scene and can identify the occluded parts of each object. Our ultimate goal is to develop a surveillance system that is free from occlusion.

Patch based frame work for occlusion detection in multi human tracking

When objects are occluded, some parts may not be visible to human vision. Object detection is a difficult job in real time tracking of multiple objects due to occlusion. This paper presents a survey on detection of occlusion. Similarly occlusion at faces may be leads to performance degradation of face recognition algorithms. Many methods were developed to detect occlusion in present and previous works. The aim of developing such intelligent system is to detect the object and identify them actively under various performance reducing parameters. The performances of the best techniques are reviewed in this paper.

/pdf/a-survey-on-occlusion-detection-44rvg21new.pdf

A Survey On Occlusion Detection

Safety applications provided by Vehicular Ad Hoc Network is very crucial. The vehicles in the road form a network enabling the passengers with infotainment and security. Emergency messages can be disseminated in the VANET scenario for securing the lives of people by avoiding the situations like chain collisions. The main aim of such messages is to provide safety. So these messages should be genuine one and propagated to all the vehicles in the scenario without any delay. In this paper, clustering technique is used along with the trust relationship to disseminate emergency messages in the network to secure the vehicles from hazardous conditions.

A secure alert messaging for vehicular ad hoc networks

Road traffic sensitive routing for efficient waste collection by leveraging varying traffic patterns

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