Advanced Evolutionary Computation and Deep Learning for Real-Time Video Target Tracking and Image Processing

Over the past few decades, new commercial markets and user requirements of video and image processing have increased dramatically. Target detection with accurate trajectory in real-time settings and high-resolution image performance are now essential in the computer vision domain. Meanwhile, evolutionary algorithms also play an important role in the optimisation and estimation of video target tracking and image processing. Some effects and challenges need to be addressed within videos and images to provide precise results in complex and changeable conditions. The aim of this research is to develop the potential of new and advanced evolutionary computation methodologies while offering concise, accurate and real-time image processing outcomes. Further, a new deep-learning approach has been proposed with excellent results to solve image super-resolution problems and blur target challenges in object moving estimation. In the first part of thesis, two particle swarm optimisation (PSO) based methods were designed and developed to achieve higher performance in calculating random object movement. One method optimises particle quality and its diversity in iteration movement with dynamic inertia weight value. The other method expands a particle’s flexibility and search domain by inheriting useful information from the previous solution to perform the dynamic particle movement. Both these approaches have been shown to improve performance, speed and ability to handle multiple moving patterns compared to other traditional methods from the testing experiments. The second part of thesis focused on multi-swarm algorithms. The interactive information exchange among multiple swarms will increase computation capability for solution-searching. Further, it will improve result quality over a single swarm. Two multi-swarm approaches were developed and tested in this research stage. The first approach is a novel multiple particle swarm with dynamic convergence approach for object tracking in complicated environments. This new approach absorbs the advantages of other multi-swarm algorithms to optimise the resources and process iteration. In addition, multiple independent populations will inherit each of their own attribute’s weights through dynamic range convergence and influence one another’s solution effects. The second multi-swarm approach distributes multiple swarms in organised grids of desired search space and dimensions to produce more accurate outcomes and wider search domains to overcome existing tracking challenges and interferences. Compared to single swarm methods in experiments, multi-swarm structure has been demonstrated to require fewer resources and iteration processes with improved performance of target focusing and retrieval. In third part, evolutionary algorithms could work to select a more representative target definition in invariant-feature optimisation. Different from the first and second stages, which focused on using swarm intelligence as a search strategy in video tracking, and instead of colour histogram representation, the proposed discrete dynamic swarm optimisation approach aims to provide a richer target description to overcome the challenges like illumination changes, background noise, deformation and occlusions. Further, the approach will integrate with a complementary procedure to eliminate inappropriate feature points geographically. According to the experiment results, target estimation can more flexibly manage various kinds of tracking challenges and provide stable detection in complicated conditions. In final part, to provide more accurate tracking performance and especially in blurry challenge, convolutional neural network is considered to solve problems associated with low-resolution images and videos through its excellent performance and recent impact in computer vision society. In this stage, a novel deep parallel residual network (DPRN) was proposed. It has been proven a fast and efficient algorithm to solve image super-resolution problems using residual learning. It demonstrated significant advantages in accuracy and speed factors compared with other approaches, with its deeper convolutional network structure, more accurate image reconstruction and real-time model execution. The proposed approach categorises layers in branches and increases the number of layers to 35 with parallel local residual learning. Further, it adapts the Adam optimiser, which can enhance the proposed system to achieve faster training speeds and improved image quality. The experiments were conducted using standard benchmark datasets such as Set5, Set14, BSDS100 and Urban100 and compared with current state-of-the-art approaches. The results indicate that the proposed DPRN can provide higher super-resolution quality with real-time execution (27.18 fps average) when compared with different state-of-the-art algorithms. Based on the useful DPRN super-resolution approach, small and blurry targets in low-resolution videos will perform better than tracking against different noises in higher-resolution environments