Robust online visual tracking

University of Technology Sydney. Faculty of Engineering and Information Technology.Visual tracking plays a key role in many computer vision systems. In this thesis, we study online visual object tracking and try to tackle challenges that present in practical tracking scenarios. Motivated by different challenges, several robust online visual trackers have been developed by taking advantage of advanced techniques from machine learning and computer vision. In particular, we propose a robust distracter-resistant tracking approach by learning a discriminative metric to handle distracter problem. The proposed metric is elaborately designed for the tracking problem by forming a margin objective function which systematically includes distance margin maximization, reconstruction error constraint, and similarity propagation techniques. The distance metric obtained helps to preserve the most discriminative information to separate the target from distracters while ensuring the stability of the optimal metric. To handle background clutter problem and achieve better tracking performance, we develop a tracker using an approximate Least Absolute Deviation (LAD)-based multi-task multi-view sparse learning method to enjoy robustness of LAD and take advantage of multiple types of visual features. The proposed method is integrated in a particle filter framework where learning the sparse representation for each view of a single particle is regarded as an individual task. The underlying relationship between tasks across different views and different particles is jointly exploited in a unified robust multi-task formulation based on LAD. In addition, to capture the frequently emerging outlier tasks, we decompose the representation matrix to two collaborative components which enable a more robust and accurate approximation. In addition, a hierarchical appearance representation model is proposed for non-rigid object tracking, based on a graphical model that exploits shared information across multiple quantization levels. The tracker aims to find the most possible position of the target by jointly classifying the pixels and superpixels and obtaining the best configuration across all levels. The motion of the bounding box is taken into consideration, while Online Random Forests are used to provide pixel- and superpixel-level quantizations and progressively updated on-the-fly. Finally, inspired by the well-known Atkinson-Shiffrin Memory Model, we propose MUlti-Store Tracker, a dual-component approach consisting of short- and long-term memory stores to process target appearance memories. A powerful and efficient Integrated Correlation Filter is employed in the short-term store for short-term tracking. The integrated long-term component, which is based on keypoint matching-tracking and RANSAC estimation, can interact with the long-term memory and provide additional information for output control

Staple: Complementary Learners for Real-Time Tracking

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.Comment: To appear in CVPR 201

Long-Term Visual Object Tracking Benchmark

We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.Comment: ACCV 2018 (Oral