So you think you can track?

• URL: http://arxiv.org/abs/2309.07268v1
• Date: Wed, 13 Sep 2023 19:18:18 GMT
• Title: So you think you can track?
• Authors: Derek Gloudemans, Gergely Zach\'ar, Yanbing Wang, Junyi Ji, Matt Nice, Matt Bunting, William Barbour, Jonathan Sprinkle, Benedetto Piccoli, Maria Laura Delle Monache, Alexandre Bayen, Benjamin Seibold, Daniel B. Work
• Abstract summary: This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics.
• Score: 37.25914081637133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding.

Related papers

• The 8th AI City Challenge [57.25825945041515]
  The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks.
  arXiv  Detail & Related papers  (2024-04-15T03:12:17Z)
• Vehicle Perception from Satellite [54.07157185000604]
  The dataset is constructed based on 12 satellite videos and 14 synthetic videos recorded from GTA-V. It supports several tasks, including tiny object detection, counting and density estimation. 128,801 vehicles are annotated totally, and the number of vehicles in each image varies from 0 to 101.
  arXiv  Detail & Related papers  (2024-02-01T15:59:16Z)
• The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking [4.799822253865053]
  This work presents a novel video dataset recorded from overlapping highway traffic cameras along an urban interstate, enabling multi-camera 3D object tracking in a traffic monitoring context. Data is released from 3 scenes containing video from at least 16 cameras each, totaling 57 minutes in length. 877,000 3D bounding boxes and corresponding object tracklets are fully and accurately annotated for each camera field of view and are combined into a spatially and temporally continuous set of vehicle trajectories for each scene.
  arXiv  Detail & Related papers  (2023-08-28T18:43:33Z)
• Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities [4.4855664250147465]
  We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. The dataset consists of 17 hours of labeled video material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night scenes.
  arXiv  Detail & Related papers  (2022-08-30T11:36:07Z)
• Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation [5.366354612549172]
  We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads. To speed up, the grayscale frame difference is used for the intermediate frames in a segment, which can double the processing speed.
  arXiv  Detail & Related papers  (2022-07-04T03:55:19Z)
• Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking [58.95210121654722]
  We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams. Our method is ranked among the top five performers on the public leaderboard.
  arXiv  Detail & Related papers  (2022-04-15T12:47:01Z)
• VariabilityTrack:Multi-Object Tracking with Variable Speed Object Movement [1.6385815610837167]
  Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. We propose a variable speed Kalman filter algorithm based on environmental feedback and improve the matching process.
  arXiv  Detail & Related papers  (2022-03-12T12:39:41Z)
• Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark [97.07865343576361]
  We construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd. We annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes. We design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds.
  arXiv  Detail & Related papers  (2021-05-06T04:46:14Z)
• Tracking Objects as Points [83.9217787335878]
  We present a simultaneous detection and tracking algorithm that is simpler, faster, and more accurate than the state of the art. Our tracker, CenterTrack, applies a detection model to a pair of images and detections from the prior frame. CenterTrack is simple, online (no peeking into the future), and real-time.
  arXiv  Detail & Related papers  (2020-04-02T17:58:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.