Detection of composite events spanning multiple camera views with wireless embedded smart cameras

Wang, Youlu; Velipasalar, Senem; Casares, Mauricio

doi:10.1109/icdsc.2009.5289355

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…It's also possible to build dedicated smart camera nodes around mobile chip sets with additional features like weather-proof and controllable orientation. Such platforms allow sophisticated image/video processings [1], [2], [3]. Face detection, for example, is one of the processings.…”

Section: Introductionmentioning

confidence: 99%

Target Domain Adaptation for Face Detection in a Smart Camera Network with Peer-to-Peer Communications

Chen¹,

Lü²,

Sun³

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

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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.

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Section: Introductionmentioning

confidence: 99%

Target Domain Adaptation for Face Detection in a Smart Camera Network with Peer-to-Peer Communications

Chen¹,

Lü²,

Sun³

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

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“…We presented a light-weight and efficient foreground detection algorithm in [3], [4], and implemented and imported it to the embedded smart camera nodes [30]. This algorithm is highly robust against lighting variations and non-static backgrounds including scenes with swaying trees, water fountains and rain.…”

Section: Light-weight Object Detection and Tracking Algorithmsmentioning

confidence: 99%

“…We define composite and semantically high-level events as a sequence of primitive events, and these primitives can be defined on different camera views [30]. The existing primitive events are motion detection and trip-wire crossing.…”

Section: Wireless Communication Between Camerasmentioning

confidence: 99%

See 1 more Smart Citation

Power consumption and performance analysis of object tracking and event detection with wireless embedded smart cameras

Casares

Pinto

Wang

et al. 2009

2009 3rd International Conference on Signal Processing and Communication Systems

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Abstract-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-topeer 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.

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“…Traditional solutions have been based on a centralized scheme where the system relies on a back-end layer of computer servers to process the video sequences. Utilizing the computational power of CITRIC, the Wang et al [2009Wang et al [ , 2010 have studied how to perform cooperative object detection and tracking based on only peer-to-peer communication, and therefore completely forgo the centralized server model. The system is designed to locally detect certain well-defined primitive events on individual CITRIC sensors.…”

Section: Other Applicationsmentioning

confidence: 99%

A low-bandwidth camera sensor platform with applications in smart camera networks

Chen

Hong

Naikal

et al. 2013

ACM Trans. Sen. Netw.

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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.

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Detection of composite events spanning multiple camera views with wireless embedded smart cameras

Cited by 8 publications

References 18 publications

Target Domain Adaptation for Face Detection in a Smart Camera Network with Peer-to-Peer Communications

Target Domain Adaptation for Face Detection in a Smart Camera Network with Peer-to-Peer Communications

Power consumption and performance analysis of object tracking and event detection with wireless embedded smart cameras

A low-bandwidth camera sensor platform with applications in smart camera networks

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