CoMaL Tracking: Tracking Points at the Object Boundaries

Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched. This is inefficient and may also lose many points that are not re-detected in the next frame. We propose a novel tracking algorithm to accurately and efficiently track CoMaL points. For this, the level line segment associated with the CoMaL points is matched to MSER segments in the next frame using shape-based matching and the matches are further filtered using texture-based matching. Experiments show improvements over a simple re-detect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on Tracking and Surveillance, CVPR 201

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

The recent introduction of powerful embedded graphics processing units (GPUs) has allowed for unforeseen improvements in real-time computer vision applications. It has enabled algorithms to run onboard, well above the standard video rates, yielding not only higher information processing capability, but also reduced latency. This work focuses on the applicability of efficient low-level, GPU hardware-specific instructions to improve on existing computer vision algorithms in the field of visual-inertial odometry (VIO). While most steps of a VIO pipeline work on visual features, they rely on image data for detection and tracking, of which both steps are well suited for parallelization. Especially non-maxima suppression and the subsequent feature selection are prominent contributors to the overall image processing latency. Our work first revisits the problem of non-maxima suppression for feature detection specifically on GPUs, and proposes a solution that selects local response maxima, imposes spatial feature distribution, and extracts features simultaneously. Our second contribution introduces an enhanced FAST feature detector that applies the aforementioned non-maxima suppression method. Finally, we compare our method to other state-of-the-art CPU and GPU implementations, where we always outperform all of them in feature tracking and detection, resulting in over 1000fps throughput on an embedded Jetson TX2 platform. Additionally, we demonstrate our work integrated in a VIO pipeline achieving a metric state estimation at ~200fps.Comment: IEEE International Conference on Intelligent Robots and Systems (IROS), 2020. Open-source implementation available at https://github.com/uzh-rpg/vili

Accelerated Object Tracking with Local Binary Features

Multi-object tracking is a problem with wide application in modern computing. Object tracking is leveraged in areas such as human computer interaction, autonomous vehicle navigation, panorama generation, as well as countless other robotic applications. Several trackers have demonstrated favorable results for tracking of single objects. However, modern object trackers must make significant tradeoffs in order to accommodate multiple objects while maintaining real-time performance. These tradeoffs include sacrifices in robustness and accuracy that adversely affect the results. This thesis details the design and multiple implementations of an object tracker that is focused on computational efficiency. The computational efficiency of the tracker is achieved through use of local binary descriptors in a template matching approach. Candidate templates are matched to a dictionary composed of both static and dynamic templates to allow for variation in the appearance of the object while minimizing the potential for drift in the tracker. Locality constraints have been used to reduce tracking jitter. Due to the significant promise for parallelization, the tracking algorithm was implemented on the Graphics Processing Unit (GPU) using the CUDA API. The tracker\u27s efficiency also led to its implantation on a mobile platform as one of the mobile trackers that can accurately track at faster than realtime speed. Benchmarks were performed to compare the proposed tracker to state of the art trackers on a wide range of standard test videos. The tracker implemented in this work has demonstrated a higher degree of accuracy while operating several orders of magnitude faster

Tools for the Development of Advanced Thermal Management Techniques for Future Safety-Critical Embedded Systems

Softwarové metody snižování teploty ukazují velký potenciál pro výpočetní platformy malých letadel pro avioniku tím, že umožňují zvýšenou spolehlivost, výkon a zmenšení velikosti a hmotnosti bez zvýšení nákladů na hardware. Pro vyhodnocení těchto metod uvádíme dvojici nástrojů pro záznam a zpracování dat z teplotních senzorů a z termální kamery při různých pracovních zatížení.. Tyto nástroje jsou pak použité k lokalizaci zdrojů tepla na čipu a k navrhování metod pro snižování teploty čipů. Nástroje splňují jejich požadavky a jsou úspěšně použité pro vyhodnocení metod snižování teploty.Software-based temperature reduction methods show great potential for small aircraft avionics computing platforms by allowing improved dependability, performance and reduction in size and weight without increasing hardware costs. To evaluate such methods, we present a pair of tools for recording and processing data from temperature sensors and a thermal camera during the execution of various workloads. These tools are then used to determine locations of on-chip heat sources and to propose methods for reducing chip temperature. The tools meet their requirements and are successfully used for the evaluation of temperature reduction methods

Analysis of Motion of People by a Stationary Camera

Hlavním cílem této práce bylo navrhnout a vytvořit systém sledování osob s aplikací v oboru bezpečnosti nebo pro analýzu chování zákazníka v obchodě. Systém byl úspěšně implementován pomocí metod KLT trekování, AdaBoost klasifikátoru a datové asociace pomocí Markovských řetězců a metody Monte Carlo. Implementace umožňuje analýzu pohybu lidí ve vnitřních i vnějších prostorech.The main goal of this thesis is to develop multi-target tracking system for use in field of security surveillance or for customer behavior analysis. The system was successfully implemented using KLT tracking, AdaBoost classifier and Markov Chain Monte Carlo data association. It is able to perform analysis of motion of people in both outdoor and indoor environment.