Multiple Vehicle detection and tracking is one of the hot research topics in the field of intelligent transportation systems, image processing, computer vision, robotics whereas applications are real time traffic monitoring, lane estimation, accident avoidance, alarm signal to indicate road accidents to save the public safety and so on. There exists a numerous higher level applications are motivated by a young researchers and scientists to identify the newly advanced techniques in which to solve the real time traffic problems using machine learning and deep learning methods to track multiple vehicles accurately. To addresses the various existing challenges in machine learning and deep learning based multiple vehicle detection and tracking algorithms namely camera oscillation, shadowing, changing in background motion, cluttering, camouflage etc. for the detection rate decreases dramatically when the distribution of the training samples and the scene target samples do not match. To address this issue, a new hybrid model of two-tier classifier of Haar+HOG, SVM+AdaBoost classifier algorithm based on a feature extraction algorithm is proposed in this paper. Inspired by the Adaptive Discrete Classifiers mechanism multiple relatively independent source samples are first used to build multiple classifiers and then particle grouping is used to generate the target training samples with confident scores. The global manual feature extraction ability of deep convolutional neural network is then used to perform source-target scene feature similarity calculation with a deep auto encoder in order to design a composite deep structure based adaptive discrete classifier and its global training method. The main contributions of this paper are threefold: 1) To improve the overall accuracy rate of multiple vehicle detection and tracking of front-view vehicles alone rather than full-sided vehicles. 2) The novelty of our proposed work is for particle grouping of multi-vehicles such as car, bus and lorry. 3) To propose the tracking of front- view multi- vehicles in linear and non-linear motion using particle and extended kalman filter along with hybrid new multi-vehicle tracking algorithm and attains 93.6% of accuracy is shown in the experimental results. We evaluates our proposed method with standard data sets PETS 2016 and 5 self-data sets iROAD were manually collected on traffic road and compared with the existing state of the art approaches and along with the Experiments on the Kitti dataset and a 3 different self -data set captured by our group demonstrate that the proposed method performs better than the existing machine-learning based vehicle detection methods. In addition, compared with the existing automatic feature extraction and region based object detection methods, our new hybrid method improves the overall detection rate by an average of approximately 5% of existing methods.
Accurate Vehicle Detection Using Multi-camera Data Fusion and Machine Learning
