Fast, Dense Feature SDM on an iPhone

In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device. Our contributions are two-fold. Drawing inspiration from the FFT, we propose a Sparse Compositional Regression (SCR) framework, which enables a significant speed up over classical dense regressors. Second, we propose a binary approximation to SIFT features. Binary Approximated SIFT (BASIFT) features, which are a computationally efficient approximation to SIFT, a commonly used feature with SDM. We demonstrate the performance of our algorithm on an iPhone 7, and show that we achieve similar accuracy to SDM

Learning Background-Aware Correlation Filters for Visual Tracking

Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the object is not be modelled over time which can result in suboptimal results. In this paper we propose a Background-Aware CF that can model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient - and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers including those based on a deep learning paradigm

An intelligent vision system for wildlife monitoring

The understanding of animal behaviour in response to human development is vital for sustainable management of ecosystems. Existing methods of monitoring wildlife activity fall short in facets pertaining to accuracy, accessibility, cost and practicality. To address this level of crudity, recent technological advances have led to the development of electronic, autonomous wildlife monitoring solutions. Whilst these developments improve the overall experience in some areas, there is still much to be desired. This dissertation aims to outline the development of an accessible, affordable, intelligent vision-based technique which addresses limitations of existing monitoring methods. A signal processing methodology was investigated, developed and implemented. The development of this methodology included the investigation of two distinct facets of computer vision - image segmentation and event classification. The existing literature was explored, and several image segmentation techniques were explored. Upon further investigation, the Gaussian Mixtures Model was selected in two forms - per pixel modelling (Zivkovic 2004) and a compressive sensing based method(Shen, Hu, Yang, Wei & Chou 2012). Each method was evaluated in terms of real time capabilities and accuracy to provide basis for recommendation of the method presented in the prototype. Upon evaluation, it was discovered that the proposed compressive sensing based method was a suitable prototype and recommendations regarding the implementation and com- missioning of the system were made. Furthermore, possible avenues for further research and development were explored

Why capture frame rate matters for embedded vision

This thesis examines the practical challenges of reliable object and facial tracking on mobile devices. We investigate the capabilities of such devices and propose a number of strategies to leverage the hardware and architectural strengths offered by smartphones and other embedded systems. We show how high frame rate cameras can be used as a resource to trade off algorithmic complexity while still achieving reliable, real time tracking performance. We also propose a number of strategies for formulating tracking algorithms, which make better use of the architectural redundancies inherent to modern system-on-chips

Need for speed: A benchmark for higher frame rate object tracking

In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking. The dataset consists of 100 videos (380K frames) captured with now commonly available higher frame rate (240 FPS) cameras from real world scenarios. All frames are annotated with axis aligned bounding boxes and all sequences are manually labelled with nine visual attributes - such as occlusion, fast motion, background clutter, etc. Our benchmark provides an extensive evaluation of many recent and state-of-the-art trackers on higher frame rate sequences. We ranked each of these trackers according to their tracking accuracy and real-time performance. One of our surprising conclusions is that at higher frame rates, simple trackers such as correlation filters outperform complex methods based on deep networks. This suggests that for practical applications (such as in robotics or embedded vision), one needs to carefully tradeoff bandwidth constraints associated with higher frame rate acquisition, computational costs of real-time analysis, and the required application accuracy. Our dataset and benchmark allows for the first time (to our knowledge) systematic exploration of such issues, and will be made available to allow for further research in this space