Roadesic Distance: Flow-Aware Tracklet Association Cost for Wide Area Surveillance

Date

2017-09-20

Author

Gürbüz, Yeti Ziya
Alatan, Abdullah Aydın

Metadata

Show full item record

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

Item Usage Stats

122
views

0
downloads

Long-term multi-target tracking via tracklet merging in wide area surveillance has crucial importance to improve tracker performances and operational requirements. Min-cost network flow formulation for multi-target tracking is adopted for the tracklet merging problem. In order to improve the continuity of the computed flows by the min-cost network flow framework, a novel tracklet association cost is proposed to be utilized in this network. The proposed cost is based on connecting two tracklets by considering the traffic flow which is estimated from the precomputed tracklets. Such an approach enforces spatial consistencies between tracks by imposing these relations into the association cost. Hence, without violating the min-cost network flow formulation, a constraint to enforce spatial consistency can be implicitly obtained. The proposed cost function can be further exploited to interpolate gaps between the merged tracklets for post processing. The experimental results show that proposed association cost improves baseline framework that uses costs considering only two tracklets at a time, as well as some other tracklet merge algorithms from the literature.

Subject Keywords

Multi-target tracking, Tracklet merging, Wide area surveillance, Roadesic distance

URI

https://hdl.handle.net/11511/54629

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Association Cost Enforcing Spatial Consistency for Tracklet Merging Gürbüz, Yeti Ziya; Alatan, Abdullah Aydın (2017-05-18) In this study, min-cost network flow formulation for multi-target tracking is adopted for the tracklet merging problem in wide area surveillance. In order to improve the continuity of the computed flows by the min-cost network flow framework, a novel tracklet association cost is proposed to be utilized in this network. The proposed cost is based on connecting two tracklets by considering the traffic flow which is estimated from the precomputed tracklets. Such an approach enforces spatial consistencies betwe...
Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle Skoglar, Per; Orguner, Umut; Tornqvist, David; Gustafsson, Fredrik (2012-07-01) This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decisio...
Target localization methods for frequency - only MIMO radar Kalkan, Yılmaz; Baykal, Buyurman; Department of Electrical and Electronics Engineering (2012) This dissertation is focused on developing the new target localization and the target velocity estimation methods for frequency-only multi-input, multi-output (MIMO) radar systems with widely separated antennas. If the frequency resolutions of the transmitted signals are enough, only the received frequencies and the Doppler shifts can be used to find the position of the target. In order to estimate the position and the velocity of the target, most multistatic radars or radar networks use multiple independen...
Evaluation of deep learning based multiple object trackers Moured, Omar; Akar, Gözde; Department of Electrical and Electronics Engineering (2020-9) 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 h...
Ferromagnetic Target Detection and Localization with a Wireless Sensor Network Antepli, Mehmet Akif; Gurbuz, Sevgi Zubeyde; Uysal, Elif (2010-11-03) This work attempts to address challenges of using magnetic sensors for target detection, localization and tracking with a wireless sensor network (WSN). A WSN comprised of magnetic sensors was constructed to investigate the modeling, detection, and localization of ferrous targets. The system was established as a centralized tree-based wireless network with a PC acting as the fusion center. A heavy cylindrical iron bar was used as a test target and modeled as a magnetic dipole. The magnetic signal models use...

Citation Formats

Y. Z. Gürbüz and A. A. Alatan, “Roadesic Distance: Flow-Aware Tracklet Association Cost for Wide Area Surveillance,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54629.