Evaluation of deep learning based multiple object trackers

Download

12625742.pdf

Date

2020-9

Author

Moured, Omar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

318
views

364
downloads

Multiple object tracking (MOT) is a significant problem in the computer vision community due to its applications, including but not limited to, surveillance and emerging autonomous vehicles. The difficulties of this problem lie in several challenges, such as frequent occlusion, interaction, intra-class variations, in-and-out objects, etc. Recently, deep learning MOT methods confront these challenges effectively. State-of-the-art deep learning (DL) trackers pipeline consists of two stages, i.e., appearance handling, which includes object detection and feature extraction, and grouping step, which performs affinity computation and data association. One of the main concerns of this thesis is to investigate how DL was employed in each one of these stages. In addition to that, we have experimented with different performance-enhancing methods, the currently top online tracker on the MOTChallenge dataset. Based on the investigation and experiments, we will identify and discuss the significant shortcomings of the current frameworks, providing possible ways to improve it.

Subject Keywords

Multiple Object Tracking, Deep Learning, Online Tracker

URI

https://hdl.handle.net/11511/69277

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Mean-Shift Tracking for Surveillance Applications Using Thermal and Visible Band Data Fusion Beyan, Cigdem; Temizel, Alptekin (2011-04-28) Separate tracking of objects such as people and the luggages they carry is important for video surveillance applications as it would allow making higher level inferences and timely detection of potential threats. However, this is a challenging problem and in the literature, people and objects they carry are tracked as a single object. In this study, we propose using thermal imagery in addition to the visible band imagery for tracking in indoor applications (such as airports, metro or railway stations). We u...
Pedestrian tracking with an infrared sensor using road network information Skoglar, Per; Orguner, Umut; Tornqvist, David; Gustafsson, Fredrik (2012-02-14) This article presents a pedestrian tracking methodology using an infrared sensor for surveillance applications. A distinctive feature of this study compared to the existing pedestrian tracking approaches is that the road network information is utilized for performance enhancement. A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (off-road) targets. The lateral position of the pedestrians on th...
Simulation and verification of security attacks on light-weight RFID protocols Ahmed, Saman; Diker Yücel, Melek; Department of Cryptography (2014) Radio Frequency Identification (RFID) technology is fast reaching all avenues of application. From retail to warehousing, tracking farm animals to monitoring medicine dosage in human body, traffic control to airport baggage control, it is penetrating all forums and industries and providing ease of deployment and automated visibility and management of inventories which was not possible with traditional barcodes. Along with its superiority over barcodes, RFID systems are also required to be costeffective to b...
Object tracking system with seamless object handover between stationary and moving camera modes Emeksiz, Deniz; Temizel, Alptekin; Department of Information Systems (2012) As the number of surveillance cameras and mobile platforms with cameras increases, automated detection and tracking of objects on these systems gain importance. There are various tracking methods designed for stationary or moving cameras. For stationary cameras, correspondence based tracking methods along with background subtraction have various advantages such as enabling detection of object entry and exit in a scene. They also provide robust tracking when the camera is static. However, they fail when the ...
Face Recognition Based on Embedding Learning Karaman, Kaan; Koc, Aykut; Alatan, Abdullah Aydın (2018-09-11) Face recognition is a key task of computer vision research that has been employed in various security and surveillance applications. Recently, the importance of this task has risen with the improvements in the quality of sensors of cameras, as well as with the increasing coverage of camera networks setup everywhere in the cities. Moreover, biometry-based technologies have been developed for the last three decades and have been available on many devices such as the mobile phones. The goal is to identify peop...

Citation Formats

O. Moured, “Evaluation of deep learning based multiple object trackers,” M.S. - Master of Science, Middle East Technical University, 2020.