Research Of Vehicle Tracking Methods Based On Particle Filter And Incremental Learning

Intelligentized trend of modern traffic transportation relates to vehicle intelligence in itself and intelligent traffic surveillance on roadway. Using vision system to track nearby vehicles helps intelligent vehicle to reasonably evade potential near or dangerous driving vehicles. On the surroundings of intelligent traffic surveillance, tracking moving vehicles on roadway helps to extract traffic informations such as traffic flow speed, occupancy rate of lanes, instantaneous vehicle speed, etc.. Particle filtering algorithm accords with the demands of nonlinear target state and non-Gaussian noise distribution, which is suitable not only for stationary vision platform but also for moving vision platform. The above advantages make it an optimum option on current tracking algorithms. On the scenes such as stochastic noises disturbing on images, vehicle blocked and vehicle speed changing, etc., the success rate and processing speed of traditional vehicle tracking algorithms are difficult to completely match the demands of vision system on intelligent vehicle. On the aspect of intelligent traffic surveillance on roadway, image-based vehicle tracking algorithms are also difficult to completely adapt to these scenes such as illumination change, shadow and rain-snow-fog weather, etc.. As the traditional predictive tracking method, particle filter algorithm itself lacks of sufficient intelligent means to dealing with vehicle tracking on complicated scenes. On above complicated scenes about vehicle tracking, the thesis emphasizes particularly on the aspect of integrating particle filter with incremental learning, researches solutions on further improving stability, realtime performance and reliability about particle filter tracking algorithm.This thesis mainly focuses on moving vehicle tracking methods on complicated scenes, main researchful work and contributions of the thesis are as follows:(1) To resolve the issue of traditional particle filter SIR algorithm which tracking stability is not good enough caused by approximate calculation of particles weights attributing to import simplified proposal distribution, based on the strong relevancy within sequential frames, particle filter tracking algorithm importing weighted sampling on pre-frames is proposed in this thesis, sampling particles on pre-frames aprior information and system state predictive information to correct information of predictable particles. The tracking results on standard testing video Carll demonstrate that the proposed algorithm can steadily track vehicle which speed is variational on road light existing scene, compared with particle filter SIR algorithm. For further decreasing errors on resampling and improving efficiency of sampling strategy on particle filter, stratified resampling method imported by residual information is proposed based on multinomial resampling method. Reasonable accumulative distributing function is constructed by importing residuals of weights on associated particles. Synchronously, sequential stochastic numbers are gradually produced by stratifing on stochastic muster. Particle filter SIR algorithms embedded by four kinds of resampling methods are tested on standard videos. Based on testing data of tracking errors and convergent particles, the tracking data indicates that particle filter tracking method embedded by the proposed resampling is best among these tracking methods.(2) Aiming at the difficulties on stably and timely tracking vehicle on the scenes such as volatile moving direction, varying pose and distance, illumination change, etc., based on autocorrelation matrix, incremental learning on IPCA and particle filter algorithm, one kind of vehicle tracking methods using appearance model is proposed, not relying on training images of vehicle in advance and not assuming the subspace mean of vehicle is fixed. When beginning at original tracking time, the proposed method can timely learn the characteristic subspace images of vehicle, using autocorrelation matrix and eigen value decomposition. Based on IPCA incremental learning, likelihood probability density is computed on subspace mean and eigenvector, increasing computational precision on weights of particles on particle filter algorithm. The number of particles is assigned as 300 and IPCA incremental learning is updated every 5 frames in this experiment. The tracking results on three standard testing videos involving Car4 demonstrate that success tracking rate of the proposed tracking method is raised to 95.1～96.4 percent, compared with 82.7～92.3 percent of P.Hall-IPCA particle filter and 92.1～95.2 percent of D.Ross-IPCA particle filter.(3) On the tracking scene involving long sequential frames, vehicle tracking process is always subjected to severe disturbances on the scenes such as object deformed, illumination change and stochastic noises, etc.. To solve the problems of distortion and excursion with window of tracking algorithm, the systemic state variables are projected to Lie group space to dealing with in this thesis, attributing to affine group invariability on disturbances. Simultaneously, the incremental learning algorithm on IPCA is used for incrementally learning and updating characteristic subspaces of the target. Based on importing measurement vector in computing weights of sampling particles on particle filtering algorithm, the method is presented to enhance computational precision about weights. Tracking window of the proposed self-adjusting tracker is not deformed by noises, and can adjust appropriate size and angle to adapting changes continuously induced by pose and distance on target. The tracking experiments based on four standard videos involving Dtneu_schnee demonstrate that success tracking rate of the proposed self-adjusting tracker is raised to 96.1～97.7 percent, compared with 91.3～95.7 percent of tracker VTD,82.9～94.2 percent of tracker IVT and 94.6～96.7 percent of tracker Kwon2010.(4) Aiming at the difficulties on tracking moving vehicle when vehicle blocked, varying on movement state, existing noise circumstance such as rain and snow, differed from unchanging prediction algorithm about tracking on vehicle over a long period of time, the SSOBI particle filter tracking algorithm is proposed, uniting these elements such as particle filter, online boosting and incremental learning, sample similarity and semi-supervised learning. Current observational data, information on adjacent frames and predictive information on systemic state are used to boost up rationality on sampling particles in particle filter, preventing proposal distribution local optimization duing to severely depending on systemic state prediction. Attributing to reasonable proposal distribution on particle filter, detection range of online incremental learning is effectively reduced, speeding up the learning and effectively solving the self-learning problem of online boosting on incremental learning process. When target vehicle is partially blocked, the tracking experiments on testing..videos involving Dtneu_winter demonstrate that success tracking rate of the proposed SSOBI particle filter algorithm is raised to 88.5-90.0 percent, compared with 66.5～73.3 percent of online boosting algorithm and 76.4-82.5 percent of semi-supervised online boosting algorithm.