Distributed data association in smart camera network via dual decomposition

Smart Camera Network (SCN) mainly has different applications based on the surveillance, security and safety with limited energy, power and memory. Normally cameras operate under limited power resources. To extend the lifetime of smart camera, it is required to reduce the energy consumption in an effective way. This paper mainly presents adaptive energy consumption of smart camera network, by assigning distributed and adaptive utility function for vision tasks. The switching of vision tasks between active, idle and sleep modes takes place based on the frequency and duty cycle. Switching of tasks is mainly done on the basis of availability of energy resources. Algorithm for Multitask allocation in smart camera network is assigned in such a way that results in the reduction of energy consumption without minute changes in the performance of the target tracing and representation. Algorithms that are assigned for each task must be effective and the weight is varied in accordance with the threshold values for energy resources. This paper represents, how a little change in the task allotment, algorithm and energy resources for smart camera results a major role in the grown of implementation of vision task and life time.

Smart camera networks are becoming a fundamental piece of our intelligent cities, buildings and homes, progressively inserting themselves in our lives. From smart surveillance systems composed of a multitude of smart camera nodes to small wearable cameras able to render a visual log of our daily experience. These devices interact with each other and with a wealth of other smart stuff, and of course the internet. This rapid development is being possible thanks to the convergence of several technologies. From advanced image sensors and vision chips to embedded vision systems capable of efficient feature extraction, image encoding and wireless transmission of the relevant visual content. This opens the door to new application domains, where video analytics and the extraction of semantic information from the scene is performed in a distributed fashion, implementing a new type of cooperative and/or collaborative vision, and even fusing visual information with other sensory data.

Smart cameras integrate sensing, processing, and communication on embedded platforms and serve as key components at the sensory edge of many Internet of Things (IoT) and cyber-physical systems (CPS) applications. Although impressive technological progress has been achieved over the last decade, smart cameras still face resource limitations, particularly when used for advanced networked data processing. This position paper briefly summarizes trends in pervasive smart cameras, presents general strategies for improving resource efficiency, and discusses the resource challenges of person re-identification; a prototypical example of surveillance applications for smart cameras based on advanced machine learning techniques. Lastly, we present our concept for resource-efficient decentralized person re-identification in smart camera networks as a work in progress.

Occlusion is one of the most difficult challenges in the area of visual tracking. We propose an occlusion handling framework to improve the performance of local tracking in a smart camera view in a multicamera network. We formulate an extensible energy function to quantify the quality of a camera’s observation of a particular target by taking into account both person–person and object–person occlusion. Using this energy function, a smart camera assesses the quality of observations over all targets being tracked. When it cannot adequately observe of a target, a smart camera estimates the quality of observation of the target from view points of other assisting cameras. If a camera with better observation of the target is found, the tracking task of the target is carried out with the assistance of that camera. In our framework, only positions of persons being tracked are exchanged between smart cameras. Thus, communication bandwidth requirement is very low. Performance evaluation of our method on challenging video sequences with frequent and severe occlusions shows that the accuracy of a baseline tracker is considerably improved. We also report the performance comparison to the state-of-the-art trackers in which our method outperforms.

Smart cameras allow pre-processing of video data on the camera instead of sending it to a remote server for further analysis. Having a network of smart cameras allows various vision tasks to be processed in a distributed fashion. While cameras may have different tasks, we concentrate on distributed tracking in smart camera networks. This application introduces various highly interesting problems. Firstly, how can conflicting goals be satisfied such as cameras in the network try to track objects while also trying to keep communication overhead low? Secondly, how can cameras in the network self adapt in response to the behavior of objects and changes in scenarios, to ensure continued efficient performance? Thirdly, how can cameras organise themselves to improve the overall network's performance and efficiency? This paper presents a simulation environment, called CamSim, allowing distributed self-adaptation and self-organisation algorithms to be tested, without setting up a physical smart camera network. The simulation tool is written in Java and hence allows high portability between different operating systems. Relaxing various problems of computer vision and network communication enables a focus on implementing and testing new self-adaptation and self-organisation algorithms for cameras to use.

Smart camera networks have recently emerged as a new class of sensor network infrastructure that is capable of supporting high-power in-network signal processing and enabling a wide range of applications. In this article, we provide an exposition of our efforts to build a low-bandwidth wireless camera network platform, called CITRIC, and its applications in smart camera networks. The platform integrates a camera, a microphone, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a wireless camera mote. With reasonably low power consumption and extensive algorithmic libraries running on a decent operating system that is easy to program, CITRIC is ideal for research and applications in distributed image and video processing. Its capabilities of in-network image processing also reduce communication requirements, which has been high in other existing camera networks with centralized processing. Furthermore, the mote easily integrates with other low-bandwidth sensor networks via the IEEE 802.15.4 protocol. To justify the utility of CITRIC, we present several representative applications. In particular, concrete research results will be demonstrated in two areas, namely, distributed coverage hole identification and distributed object recognition.

Abstract-With the fast advance of mobile chips technologies, a node in a smart camera network can afford sophisticated processing via on-board multicore CPUs and GPUs, e.g., face detection. The performance of a general purpose face detector, however, may degrade seriously under specific situations with unexpected challenges such as facial coverage or bad illumination. This degradation is due to the difference of probability distributions between training data and testing data, known as source data domain and target data domain, respectively. To better adapt a smart camera network to a specific situation, some form of target domain adaptation is needed, which usually requires both the source domain data and as much as possible target domain data at each node, which may strain storage capacity and bandwidth. In this paper, we propose an adaptation method which fuses the source specific hypotheses (SSHs) and target specific hypotheses (TSHs) - requiring only a pre-trained face detector and a few target data to be shared by peer-to-peer communications, thus relieving the storage and bandwidth constraints. The method uses the accuracy-regularization objective as the adaptation model, to fuse SSHs and TSHs, and tries to minimize the misclassification error on target data. With an existing frontal face detector, we conduct experiments to verify our algorithm, covering cases of video surveillance, extreme pose challenge, and different illumination spectra. Significant performance gains are observed with only dozens of target data in all the experiments, demonstrating the effectiveness of the proposed adaptation model. Therefore, the proposed adaption can be applied to a smart camera network with peer-to-peer communications to improve the network's overall face detection performance.

This paper presents an innovative concept for a distributed system that combines a smart camera network with semantic reasoning. The proposed system is context sensitive and combines the information extracted by the smart camera with logic rules and knowledge of what the camera observes, building information and events that may occurred. The proposed system is a justification for the use of smart cameras, and it can improve the classical visual sensor networks (VSN) and enhance the standard computer vision approach. The main application of our system is smart building management, where we specifically focus on increasing the services of the building users.

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. With battery-powered and embedded smart cameras, it has become viable to install many spatially-distributed cameras interconnected by wireless links. Not requiring to have access to electrical outlets and have wired links increase system flexibility. However, wireless and battery-powered smart-camera networks introduce many additional challenges since they have very limited resources, such as power, memory and bandwidth. The algorithms running on the camera boards should be lightweight and efficient. In addition, the frequency of communication between camera nodes, and the content of the message packets should be carefully designed, since communication consumes power. In this paper, we present a wireless embedded smart-camera system that performs peer-to-peer object tracking and event detection. We analyze the power consumption and performance of this system during different parts of the algorithm execution and for different message exchanges between camera nodes. We also present a graph of the energy consumption for different tasks performed in a camera's processor. The number of instructions are also presented. The results demonstrate the importance of the careful choice of when and what data to transfer between cameras, and also the necessity of having lightweight algorithms in these resource-constrained systems.

The goal of the stereo algorithm is to determine the depth of objects from a stereo pair of images. The procedure of matching homogenous scenes from both images has been considered to be the most difficult problem. The approach described in this paper integrates the processes of feature matching and relational tree-matching techniques. Integration is necessary to ensure the high rate of correct matching results even in regions with many occlusions. The feature- matching technique used here is an adaptive one based on the boundary information from LoG operator, local invariant information detected by FFT operator, and the local curve signature. The relational tree technique is applied when the occlusion occurs. Experimental results are given for a typical photogrammetric stereo image.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Smart camera networks are becoming increasingly popular in a number of application domains. In many applications, cameras are required to collaboratively track objects (e.g., habitat monitoring, or surveillance). In smart networks, camera coverage control is necessary to allow automatic tracking of targets without human intervention, allowing these systems to scale. In this paper, we consider the problem of automatic control of the cameras to maximize coverage of a set of targets. We formulate an optimization problem with the goal of maximizing the number of covered targets. Since the optimization problem is NP-hard, even for static targets, we propose a computationally efficient heuristic to reach near-optimal solution. Centralized solutions achieve excellent coverage, and can work well for small-scale networks, however they require significant communication cost for large scale networks. As a result, we propose an algorithm that spatially decomposes the network and computes optimal solutions for individual partitions. By decomposing the partitions in a way that minimizes dependencies between them, this approach results in coverage quality close to the centralized optimal solution, with an overhead and reaction time similar to those of distributed solutions.

Smart camera networks (SCNs) are increasingly used in applications such as homeland security, border control and traffic monitoring. The cameras are often wireless with an organically growing structure, to reduce the overhead of deployment. We frame a new and important problem in SCNs: how to transmit videos from multiple cameras with overlapping coverage given the limited available wireless bandwidth to maximize the quality of the received videos. We call this problem Coordinated Transport of Correlated Videos (CTCV). CTCV is a more general version of 3D video transport: in that problem, highly correlated videos from two cameras (that provide the 3D perspective) are jointly encoded exploiting their pre-defined and known overlap. In contrast, in CTCV there is an arbitrary number of cameras whose overlap is not known apriori and that require transmission as multiple video streams. To effectively support CTCV, we propose a video delivery protocol that consists of two primary components: (1) Consolidation of correlated videos from multiple cameras which removes spatially redundant fields-of-view; and (2) Network and coverage aware bandwidth allocation to optimize coverage quality cooperatively among the different video streams to match the available bandwidth. We formulate the problem of optimal bandwidth allocation for maximizing coverage. We propose and investigate different heuristic policies for bandwidth allocation. We evaluate CTCV using data from a small camera testbed as well as topologies from realistic deployments. Experiments show that CTCV achieves around 10 dB gains in video quality in the scenarios we consider.

Single-Camera Video Surveillance (SCVS) has mainly been applied in the passive monitoring. Video surveillance covers small area, the video information is not sufficient without the joint tracking capability. Currently poor performance in most similar networks is mainly due to unintelligence and low real-time. In this paper we proposed a distributed smart multi-eyes camera network by utilizing smart phones, which presents not only the schemes but also its concrete realization. Using the smart camera network with good self-organization in time, the target would be captured and tracked in good manner and the running track for the target can be restored and recovered anytime. This work is aimed at utilizing unused mobile phones to form a security network. The contribution is aimed at improving the accuracy stranger invasion detection. In order to finish the task of recognizing faces of distant targets, we use PCA-based facial recognition algorithm to enhance the effective resolution. We category different approaches of increasing the resolution and use the best approach to handle different situation. In the mean time we designed and realized the overall structure of the security network including database module design, client terminal design and monitoring node design, forming a robust and energy saving network. This paper can provide valuable reference for the design of the practical security network which is currently in high social demand.

In this work, a framework of feature distribution scheme is proposed for object matching. In this approach, information is distributed in such a way that each individual node maintains only a small amount of information about the objects seen by the network. Nevertheless, this amount is sufficient to efficiently route queries through the network without any degradation of the matching performance. Digital image processing approaches have been investigated to reconstruct a high resolution image from aliased low resolution images. The accurate registrations between low resolution images are very important to the reconstruction of a high resolution image. The proposed feature distribution scheme results in far lower network traffic load. To achieve the maximum performance as with the full distribution of feature vectors, a set of requirements regarding abstraction, storage space, similarity metric and convergence has been proposed to implement this work in C and QT.

Single-Camera Video Surveillance now mainly taken the passive monitoring, video surveillance covers small area, the video information is not sufficient without the joint tracking capability. Poor performance is mainly due to unintelligence and low real-time. Distributed information transmission network architecture, the Paper provides a distributed smart multi-eyes camera network, which presents not only the schemes but also its concrete realization. Using the smart camera network with good self-organization in time, the target would be captured and tracked in good manner and the running track for the target can be restored and recovered anytime. It can play an important role in urban traffic environment and video monitoring in public places, which carries a wide range of social and economic benefits.