High quality face image acquisition from huge video data obtained in visual sensor network is of great significance in applications related to face processing, such as face recognition and reconstruction. This paper proposes an optimal face image acquisition method in visual sensor network, which is based on collaborative face frames acquisition and heterogeneous feature fusion-based face quality assessment. Gaussian-probabilitydistribution-based multi-view data fusion and kalman filter are used for collaborative target localization and tracking. To achieve primary screening of face frames, a lightweight face frames quality evaluation method is presented. Importantly, new face quality assessment criterion calculation methods are proposed to make fine screening of face images more applicable in visual sensor network. The new face quality assessment criterion calculation methods are based on heterogeneous feature fusion of pedestrian tracking and static face image features analysis. Fuzzy inference engine is used to combine these criteria to generate a face quality assessment score. Experimental results show that the proposed method can acquire optimal face images accurately and robustly.
We report the γ-ray ionizing radiation response of commercial off-the-shelf (COTS) monolithic active-pixel sensors (MAPS) with different integration times and gains. The distribution of the eight-bit two-dimensional matrix of MAPS output frame images was studied for different parameter settings and dose rates. We present the first results of the effects of these parameters on the response of the sensor and establish a linear relationship between the average response signal and radiation dose rate in the high-dose rate range. The results show that the distribution curves can be separated into three ranges. The first range is from 0 to 24, which generates the first significant low signal peak. The second range is from 25 to 250, which shows a smooth gradient change with different integration times, gains, and dose rates. The third range is from 251 to 255, where a final peak appears, which has a relationship with integral time, gain, and dose rate. The mean pixel value shows a linear dependence on the radiation dose rate, albeit with different calibration constants depending on the integration time and gain. Hence, MAPS can be used as a radiation monitoring device with good precision.
Due to huge amount of visual surveillance data, network congestion and latency is serious problem for surveillance applications. To reduce the data amount, data compression must be done before transmission. Compressive sensing is a new theory that sample and compress signals at the same time with little cost of bandwidth and energy for transmission. While conventional compressive sensing requires huge memory to compress whole image at a time, block compressive sensing is introduced which compress image in block manners. Based on that, region enhancement block compressive sensing is proposed to adaptive compress image sequence and enhance the quality of region of interest with reduced data transmission. In proposed scheme, a target detecting algorithm is used to select the important blocks and more number of measurements is allocated on them. Thus the transmission amount is adaptively adjusted with the target in or out. The proposed scheme is evaluated in practical surveillance data and the result demonstrates that it adjusts the measurement ratio as needed and the quality of region of interest is enhanced at reduced measurement ratio.
Index Terms -compressive sensing; block compressed sensing; image compression; visual surveillance978-1-4799-5825-2/14/$31.00
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.