Adaptive tracking via multiple appearance models and multiple linear searches

We introduce a unified tracker (FMCMC-MM) which adapts to changes in target appearance by combining two popular generative models: templates and histograms, maintaining multiple instances of each in an appearance pool, and enhances prediction by utilising multiple linear searches. These search directions are sparse estimates of motion direction derived from local features stored in a feature pool. Given only an initial template representation of the target, the proposed tracker can learn appearance changes in a supervised manner and generate appropriate target motions without knowing the target movement in advance. During tracking, it 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 effectiveness of the approach is demonstrated using a variety of challenging video sequences. Results show that this framework outperforms existing 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

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

Online Domain Adaptation for Multi-Object Tracking

Automatically detecting, labeling, and tracking objects in videos depends first and foremost on accurate category-level object detectors. These might, however, not always be available in practice, as acquiring high-quality large scale labeled training datasets is either too costly or impractical for all possible real-world application scenarios. A scalable solution consists in re-using object detectors pre-trained on generic datasets. This work is the first to investigate the problem of on-line domain adaptation of object detectors for causal multi-object tracking (MOT). We propose to alleviate the dataset bias by adapting detectors from category to instances, and back: (i) we jointly learn all target models by adapting them from the pre-trained one, and (ii) we also adapt the pre-trained model on-line. We introduce an on-line multi-task learning algorithm to efficiently share parameters and reduce drift, while gradually improving recall. Our approach is applicable to any linear object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive "off-the-shelf" ConvNet features. We quantitatively measure the benefit of our domain adaptation strategy on the KITTI tracking benchmark and on a new dataset (PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.Comment: To appear at BMVC 201