Deep Convolutional Correlation Particle Filter for Visual Tracking

In this dissertation, we explore the advantages and limitations of the application of sequential Monte Carlo methods to visual tracking, which is a challenging computer vision problem. We propose six visual tracking models, each of which integrates a particle filter, a deep convolutional neural network, and a correlation filter. In our first model, we generate an image patch corresponding to each particle and use a convolutional neural network (CNN) to extract features from the corresponding image region. A correlation filter then computes the correlation response maps corresponding to these features, which are used to determine the particle weights and estimate the state of the target. We then introduce a particle filter that extends the target state by incorporating its size information. This model also utilizes a new adaptive correlation filtering approach that generates multiple target models to account for potential model update errors. We build upon that strategy to devise an adaptive particle filter that can decrease the number of particles in simple frames in which there is no challenging scenarios and the target model closely reflects the current appearance of the target. This strategy allows us to reduce the computational cost of the particle filter without negatively impacting its performance. This tracker also improves the likelihood model by generating multiple target models using varying model update rates based on the high-likelihood particles. We also propose a novel likelihood particle filter for CNN-correlation visual trackers. Our method uses correlation response maps to estimate likelihood distributions and employs these likelihoods as proposal densities to sample particles. Additionally, our particle filter searches for multiple modes in the likelihood distribution using a Gaussian mixture model. We further introduce an iterative particle filter that performs iterations to decrease the distance between particles and the peaks of their correlation maps which results in having a few more accurate particles in the end of iterations. Applying K-mean clustering method on the remaining particles determine the number of the clusters which is used in evaluation step and find the target state. Our approach ensures a consistent support for the posterior distribution. Thus, we do not need to perform resampling at every video frame, improving the utilization of prior distribution information. Finally, we introduce a novel framework which calculates the confidence score of the tracking algorithm at each video frame based on the correlation response maps of the particles. Our framework applies different model update rules according to the calculated confidence score, reducing tracking failures caused by model drift. The benefits of each of the proposed techniques are demonstrated through experiments using publicly available benchmark datasets

Adaptive visual tracking via multiple appearance models and multiple linear searches

This research is concerned with adaptive, probabilistic single target tracking algorithms. Though visual tracking methods have seen significant improvement, sustained ability to capture appearance changes and precisely locate the target during complex and unexpected motion remains an open problem. Three novel tracking mechanisms are proposed to address these challenges. The first is a Particle Filter based Markov Chain Monte Carlo method with sampled appearances (MCMC-SA). This adapts to changes in target appearance by combining two popular generative models: templates and histograms, maintaining multiple instances of each in an appearance pool. The proposed tracker automatically switches between models in response to variations in target appearance, exploiting the strengths of each model component. New models are added, automatically, as necessary. The second is a Particle Filter based Markov Chain Monte Carlo method with motion direction sampling, from which are derived two variations: motion sampling using a fixed direction of the centroid of all features detected (FMCMC-C) and motion sampling using kernel density estimation of direction (FMCMC-S). This utilises sparse estimates of motion direction derived from local features detected from the target. The tracker captures complex target motions efficiently using only simple components. The third tracking algorithm considered here combines these above methods to improve target localisation. This tracker comprises multiple motion and appearance models (FMCMC-MM) and automatically selects an appropriate motion and appearance model for tracking. The effectiveness of all three tracking algorithms is demonstrated using a variety of challenging video sequences. Results show that these methods considerably improve tracking performance when compared with state of the art appearance-based tracking frameworks

Adaptive visual tracking via multiple appearance models and multiple linear searches

This research is concerned with adaptive, probabilistic single target tracking algorithms. Though visual tracking methods have seen significant improvement, sustained ability to capture appearance changes and precisely locate the target during complex and unexpected motion remains an open problem. Three novel tracking mechanisms are proposed to address these challenges. The first is a Particle Filter based Markov Chain Monte Carlo method with sampled appearances (MCMC-SA). This adapts to changes in target appearance by combining two popular generative models: templates and histograms, maintaining multiple instances of each in an appearance pool. The proposed tracker automatically switches between models in response to variations in target appearance, exploiting the strengths of each model component. New models are added, automatically, as necessary. The second is a Particle Filter based Markov Chain Monte Carlo method with motion direction sampling, from which are derived two variations: motion sampling using a fixed direction of the centroid of all features detected (FMCMC-C) and motion sampling using kernel density estimation of direction (FMCMC-S). This utilises sparse estimates of motion direction derived from local features detected from the target. The tracker captures complex target motions efficiently using only simple components. The third tracking algorithm considered here combines these above methods to improve target localisation. This tracker comprises multiple motion and appearance models (FMCMC-MM) and automatically selects an appropriate motion and appearance model for tracking. The effectiveness of all three tracking algorithms is demonstrated using a variety of challenging video sequences. Results show that these methods considerably improve tracking performance when compared with state of the art appearance-based tracking frameworks

Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles

We present a novel, realtime algorithm to compute the trajectory of each pedestrian in moderately dense crowd scenes. Our formulation is based on an adaptive particle filtering scheme that uses a multi-agent motion model based on velocity-obstacles, and takes into account local interactions as well as physical and personal constraints of each pedestrian. Our method dynamically changes the number of particles allocated to each pedestrian based on different confidence metrics. Additionally, we use a new high-definition crowd video dataset, which is used to evaluate the performance of different pedestrian tracking algorithms. This dataset consists of videos of indoor and outdoor scenes, recorded at different locations with 30-80 pedestrians. We highlight the performance benefits of our algorithm over prior techniques using this dataset. In practice, our algorithm can compute trajectories of tens of pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per second). To the best of our knowledge, our approach is 4-5 times faster than prior methods, which provide similar accuracy