DSP embedded smart surveillance sensor with robust SWAD-based tracker

Smart video analytics algorithms can be embedded within surveillance sensors for fast in-camera processing. This paper presents a DSP embedded video analytics system for object and people tracking, using a PTZ camera. The tracking algorithm is based on adaptive template matching and it employs a novel Sum of Weighted Absolute Differences. The video analytics is implemented on the DSP board DM6437 EVM and it automatically controls the PTZ camera, to keep the target central to the field of view. The EVM is connected to the network and the tracking algorithm can be remotely activated, so that the PTZ enhanced with the DSP embedded video analytics becomes a smart surveillance sensor. The system runs in real-time and simulation results demonstrate that the described SWAD outperforms other template matching measures in terms of efficiency and accuracy

Particle swarm variants: standardized convergence analysis

This paper presents an objective function specially designed for the convergence analysis of a number of particle swarm optimization (PSO) variants. It was found that using a specially designed objective function for convergence analysis is both a simple and valid method for performing assumption free convergence analysis. It was also found that the canonical particle swarm's topology did not have an impact on the parameter region needed to ensure convergence. The parameter region needed to ensure convergent particle behavior was empirically obtained for the fully informed PSO, the bare bones PSO, and the standard PSO 2011 algorithm. In the case of the bare bones PSO and the standard PSO 2011 the region needed to ensure convergent particle behavior di ers from previous theoretical work. The di erence in the obtained regions in the bare bones PSO is a direct result of the previous theoretical work relying on simplifying assumptions, speci - cally the stagnation assumption. A number of possible causes for the discrepancy in the obtained convergent region for the standard PSO 2011 are given.http://link.springer.com/journal/117212016-09-30hb201

A generalized theoretical deterministic particle swarm model

A number of theoretical studies of particle swarm optimization (PSO) have been done to gain a better understanding of the dynamics of the algorithm and the behavior of the particles under different conditions. These theoretical analyses have been performed for both the deterministic PSO model and more recently for the stochastic model. However, all current theoretical analyses of the PSO algorithm were based on the stagnation assumption, in some form or another. The analysis done under the stagnation assumption is one where the personal best and neighborhood best positions are assumed to be non-changing. While analysis under the stagnation assumption is very informative, it could never provide a complete description of a PSO’s behavior. Furthermore, the assumption implicitly removes the notion of a social network structure from the analysis. This paper presents a generalization to the theoretical deterministicPSOmodel. Under the generalized model, conditions for particle convergence to a point are derived. The model used in this paper greatly weakens the stagnation assumption, by instead assuming that each particle’s personal best and neighborhood best can occupy an arbitrarily large number of unique positions. It was found that the conditions derived in previous theoretical deterministic PSO research could be obtained as a specialization of the new generalized model proposed. Empirical results are presented to support the theoretical findings.http://link.springer.com/journal/11721hb201

Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption

This paper presents an extension of the state of the art theoretical model utilized for understanding the stability criteria of the particles in particle swarm optimization algorithms. Conditions for order-1 and order-2 stability are derived by modeling, in the simplest case, the expected value and variance of a particle’s personal and neighborhood best positions as convergent sequences of random variables. Furthermore, the condition that the expected value and variance of a particle’s personal and neighborhood best positions are convergent sequences is shown to be a necessary condition for order-1 and order-2 stability. The theoretical analysis presented is applicable to a large class of particle swarm optimization variants.http://link.springer.com/journal/117212019-03-01hj2017Computer Scienc