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Camera networks are an important component of modern complex systems, be it for surveillance, human/machine interaction or healthcare. Having smart cameras that can, by themselves, perform part of the data processing improves scalability both in processing and network resources. In this paper, we present the HYBRID algorithm for multiple person tracking intended for implementation on a smart camera platform, along with the development methodology to implement said algorithm in an FPGA-based smart camera. The HYBRID strategy outperforms the well-known Markov Chain Monte Carlo based particle filter (MCMC-PF) in terms of (i) parallelization capabilities as the MCMC-PF sequentially processes the particles, and (ii) tracking performances (i.e., robustness and precision).