Recent technology and market trends have demanded the significant need for feasible solutions to video/camera systems and analytics. This paper provides a comprehensive account on theory and application of intelligent video systems and analytics. It highlights the video system architectures, tasks, and related analytic methods. It clearly demonstrates that the importance of the role that intelligent video systems and analytics play can be found in a variety of domains such as transportation and surveillance. Research directions are outlined with a focus on what is essential to achieve the goals of intelligent video systems and analytics.

Intelligent Video Systems and Analytics: A Survey

Many real-world optimization problems are difficult to solve as they do not possess the nice mathematical properties required by the exact algorithms. Evolutionary algorithms are proven to be appropriate for such problems. In this paper, we propose an improved differential evolution algorithm that uses a mix of different mutation operators. In addition, the algorithm is empowered by a covariance adaptation matrix evolution strategy algorithm as a local search. To judge the performance of the algorithm, we have solved well-known benchmark as well as a variety of real-world optimization problems. The real-life problems were taken from different sources and disciplines. According to the results obtained, the algorithm shows a superior performance in comparison with other algorithms that also solved these problems.

An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems

Perception techniques for autonomous driving should be adaptive to various environments. In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system. To address these problems, in this paper, we propose a traffic line detection method called Point Instance Network (PINet); the method is based on the key points estimation and instance segmentation approach. The PINet includes several stacked hourglass networks that are trained simultaneously. Therefore the size of the trained models can be chosen according to the computing power of the target environment. We cast a clustering problem of the predicted key points as an instance segmentation problem; the PINet can be trained regardless of the number of the traffic lines. The PINet achieves competitive accuracy and false positive on the TuSimple and Culane datasets, popular public datasets for lane detection. Our code is available at this https URL

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection.

The analytic hierarchy process (AHP) has been widely applied in the last four decades, and a fuzzy logic version, the fuzzy AHP (FAHP), has also been employed in many studies since its formulation. However, it is not clear whether the FAHP is better than the AHP in terms of quality of the solution. First, the study demonstrates that the FAHP can provide a different solution than the AHP, regardless of which solution is better. However, it is also shown that such a difference does not provide an additional advantage over the AHP when a decision is proposed. Although the FAHP is a well-proven method, this study provides an insight on its practical applicability.

When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process

This paper presents a novel adaptive Gravitational Search Algorithm (GSA) for the optimal tuning of fuzzy controlled servo systems characterized by second-order models with an integral component and variable parameters. The objective functions consist of the output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The proposed adaptive GSA solves the optimization problems resulting in a new generation of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs) with a reduced time constant sensitivity. A design method for T-S PI-FCs is then proposed and experimentally validated in the representative case study of the optimal tuning of T-S PI-FCs for the position control system of a servo system.

Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems

Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes

The exiting covariance matching method is not suited for real-time applications due to its demand for exhaustive search. Aiming at this problem, we developed a novel approach based on fuzzy genetic algorithm (GA) to boost the computing efficiency of covariance matching. The approach employs GA in searching for optimal solution in a large image region. To avoid premature convergence or local optimum which often occur in traditional GAs, we use a fuzzy inference system to adaptively estimate the crossover and mutation probabilities to gain convergence in a much higher speed than using a conventional GA. Experimental results show that the proposed approach can significantly improve the processing speed of covariance matching, while keeping the matching results almost unchanged. The runtime performance of the proposed approach is faster than its counterparts using exhaustive search with eight times and more.

Fast Covariance Matching With Fuzzy Genetic Algorithm

Target tracking for mobile robot platforms via object matching and background anti-matching

Toward the unsupervised clustering for color logo images corrupted by noise, we propose a novel framework in which the logo images are described by a model called singular values based region covariance matrices (SVRCM), and the mean shift algorithm is performed on Lie groups for clustering covariance matrices. To decrease the influence of noise, we choose the larger singular values, which can better represent the original image and discard the smaller singular values. Therefore, the chosen singular values are grouped and fused by a covariance matrix to form a SVRCM model that can represent the correlation and variance between different singular value features to enhance the discriminating ability of the model. In order to cluster covariance matrices, which do not lie on Euclidean space, the mean shift algorithm is performed on manifolds by iteratively transforming points between the Lie group and Lie algebra. Experimental results on 38 categories of logo images demonstrate the superior performance of the proposed method whose clustering rate can be achieved at 88.55%.

Xuguang Zhang

Papers

Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes

Fast Covariance Matching With Fuzzy Genetic Algorithm

Target tracking for mobile robot platforms via object matching and background anti-matching

Unsupervised clustering for logo images using singular values region covariance matrices on Lie groups

A grid infrastructure for hybrid simulations.