PHACT: parallel HOG and correlation tracking

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.Comment: 8 pages, 7 figure

Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches

Critical Overview of Visual Tracking with Kernel Correlation Filter

With the development of new methodologies for faster training on datasets, there is a need to provide an in-depth explanation of the workings of such methods. This paper attempts to provide an understanding for one such correlation filter-based tracking technology, Kernelized Correlation Filter (KCF), which uses implicit properties of tracked images (circulant matrices) for training and tracking in real-time. It is unlike deep learning, which is data intensive. KCF uses implicit dynamic properties of the scene and movements of image patches to form an efficient representation based on the circulant structure for further processing, using properties such as diagonalizing in the Fourier domain. The computational efficiency of KCF, which makes it ideal for low-power heterogeneous computational processing technologies, lies in its ability to compute data in high-dimensional feature space without explicitly invoking the computation on this space. Despite its strong practical potential in visual tracking, there is a need for an in-depth critical understanding of the method and its performance, which this paper aims to provide. Here we present a survey of KCF and its method along with an experimental study that highlights its novel approach and some of the future challenges associated with this method through observations on standard performance metrics in an effort to make the algorithm easy to investigate. It further compares the method against the current public benchmarks such as SOTA on OTB-50, VOT-2015, and VOT-2019. We observe that KCF is a simple-to-understand tracking algorithm that does well on popular benchmarks and has potential for further improvement. The paper aims to provide researchers a base for understanding and comparing KCF with other tracking technologies to explore the possibility of an improved KCF tracker