© Copyright Notice: It is important that you read and understand the copyright of the following software packages as specified in the individual items. The copyright varies with each package due to its contributor(s). The packages should NOT be used for any commercial purposes without direct consent of their author(s). 

	Sensor Platform: CITRIC (Cal/ITRI Camera Mote) Phoebus Chen, Parvez Ahammad, Colby Boyer, Shih-I Huang, Leon Lin, Edgar Lobaton, Marci Meingast, Songhwai Oh, Simon Wang, Posu Yan, Allen Yang, Chuohao Yeo, Lung-Chung Chang, Doug Tygar, and Shankar Sastry. CITRIC: A low-bandwidth wireless camera network platform. ICDSC, 2008. [PDF]
	Figure 1: Left: Camera daughter board with major functional units outlined. Right: Assembled camera daughter board with Tmote sensor network board.
	Figure 2: Average run time of basic image processing functions available on the CITRIC mote. All experiments are on 512-by-512 images. Execution time at 520 MHz processor speed is shown in parentheses.
	Applications
	Distributed Object Recognition in Band-Limited Camera Sensor Networks We study the classical problem of object recognition in low-power, low-bandwidth distributed camera networks. We propose an effective framework to perform distributed object recognition using a network of smart cameras and a computer as the base station. Due to the limited bandwidth between the cameras and the computer, the method utilizes the available computational power on the smart sensors to locally extract and compress SIFT-type image features to represent individual camera views. In particular, we show that between a network of cameras, high-dimensional SIFT histograms share a joint sparse pattern corresponding to a set of common features in 3-D. Such joint sparse patterns can be explicitly exploited to accurately encode the distributed signal via random projection, which is unsupervised and independent to the sensor modality. On the base station, we study multiple decoding schemes to simultaneously recover the multiple-view object features based on the distributed compressive sensing theory. The system has been implemented on the Berkeley CITRIC smart camera platform. The efficacy of the algorithm is validated through extensive simulation and experiments. Allen Yang, Subhransu Maji, Kirak Hong, Posu Yan, and Shankar Sastry. Distributed compression and fusion of nonnegative sparse signals for multiple-view object recognition. Information Fusion, 2009. [PDF] Allen Yang, Subhransu Maji, Mario Christoudas, Trevor Darrell, Jitendra Malik, and Shankar Sastry. Multiple-view object recognition in band-limited distributed camera networks. ICDSC, 2009. [PDF]
	Demonstrations
	Counter-sniper Visual Surveillance. [download video demo] Recovery of Coverage Topology via Motion Tracking. [download video demo]
	Impacts
	We are thrilled to provide technical support to research teams in the following institutions to adopt the CITRIC platform in their projects:
	University of Nebraska