HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Jonathon Luiten,Aljosa Osep,Patrick Dendorfer,Philip H. S. Torr,Andreas Geiger,Laura Leal-Taixé,Bastian Leibe +6 more
Reads0
Chats0
TLDR
This work presents a novel MOT evaluation metric, higher order tracking accuracy (HOTA), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers.Abstract:
Multi-Object Tracking (MOT) has been notoriously difficult to evaluate. Previous metrics overemphasize the importance of either detection or association. To address this, we present a novel MOT evaluation metric, HOTA (Higher Order Tracking Accuracy), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers. HOTA decomposes into a family of sub-metrics which are able to evaluate each of five basic error types separately, which enables clear analysis of tracking performance. We evaluate the effectiveness of HOTA on the MOTChallenge benchmark, and show that it is able to capture important aspects of MOT performance not previously taken into account by established metrics. Furthermore, we show HOTA scores better align with human visual evaluation of tracking performance.read more
Citations
More filters
Proceedings ArticleDOI
Learning a Proposal Classifier for Multiple Object Tracking
TL;DR: LPC_MOT as mentioned in this paper proposes a two-stage object detector Faster RCNN to solve the data association problem in an end-to-end fashion, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph.
Proceedings ArticleDOI
Kubric: A scalable dataset generator
Klaus Greff,Francois Belletti,Lucas Beyer,Charles Doersch,Yilun Du,Daniel Duckworth,David J. Fleet,Danushen Gnanapragasam,Florian Golemo,Charles Herrmann,T. Kipf,Abhijit Kundu,Dmitry Lagun,Issam H. Laradji,Hsueh-Ti Liu,H. Meyer,Yishu Miao,Derek Nowrouzezahrai,Cengiz Oztireli,Etienne Pot,Noha Hamdy Radwan,Daniel Rebain,Sara Sabour,Mehdi Sajjadi,Matan Sela,Vincent Sitzmann,Austin Stone,Deqing Sun,Suhani Vora,Ziyu Wang,Tianhao Wu,Kwang Moo Yi,Fangcheng Zhong,Andrea Tagliasacchi +33 more
TL;DR: Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data is introduced.
Journal ArticleDOI
StrongSORT: Make DeepSORT Great Again
TL;DR: This paper revisits the classic tracker DeepSORT and upgrades it from various aspects, i.e., detection, embedding and association, and proposes Gaussian-smoothed interpolation (GSI) to compensate for missing detections.
Journal ArticleDOI
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
TL;DR: It is shown that a simple motion model can obtain state-of-theart tracking performance without other cues like appearance and is named as Observation-Centric SORT, OC-SORT for short, which remains simple, online, and real-time but improves robustness over occlusion and nonlinear motion.
Proceedings ArticleDOI
Global Tracking Transformers
TL;DR: A novel transformer-based architecture for global multi-object tracking that takes a short sequence of frames as input and produces global trajectories for all objects, and seamlessly integrates into state-of-the-art large-vocabulary detectors to track any objects.
References
More filters
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Journal ArticleDOI
The Pascal Visual Object Classes (VOC) Challenge
TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
Proceedings ArticleDOI
Are we ready for autonomous driving? The KITTI vision benchmark suite
TL;DR: The autonomous driving platform is used to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection, revealing that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world.
Journal ArticleDOI
An algorithm for tracking multiple targets
TL;DR: An algorithm for tracking multiple targets in a cluttered environment is developed, capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports.