2 research outputs found

    Multi-target pig tracking algorithm based on joint probability data association and particle filter

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    In order to evaluate the health status of pigs in time, monitor accurately the disease dynamics of live pigs, and reduce the morbidity and mortality of pigs in the existing large-scale farming model, pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs. However, it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets. In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig, this study proposed a method that used color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter. Experimental results show the proposed algorithm can quickly and accurately track pigs in the video, and it is able to cope with partial occlusions and recover the tracks after temporary loss

    Tracking-by-detection of multiple persons by a resample-move particle filter

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    International audienceCamera networks make an important component of modern complex perceptual systems with widespread applications spanning surveillance, human/machine interaction and healthcare. Smart cameras that can perform part of the perceptual data processing improve scalability in both processing power and network resources. Based on these insights, this paper presents a particle filter for multiple person tracking designed for an FPGA-based smart camera. We propose a new joint Markov Chain Monte Carlo-based particle filter (MCMC-PF) with short Markov chains, devoted to each individual particle, in order to sample the particle swarm in relevant regions of the high dimensional state-space with increased particle diversity. Finding an efficient sampling method has become another challenge when designing particle filters, especially for those devoted to more than two or three targets. A proposal distribution, combining diffusion dynamics, learned HOG + SVM person detections, and adaptive background mixture models, limits here the well-known burst in terms of particles and MCMC iterations. This informed proposal based on saliency maps has only been marginally used in the literature in a joint state space PF framework. The presented qualitative and quantitative results—for proprietary and public video datasets—clearly show that our tracker outperforms the well-known MCMC-PF in terms of (1) tracking performances, i.e. robustness and precision, and (2) parallelization capabilities as the MCMC-PF processes the particles sequentially
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