Multi Target Tracking from Drones by Learning from Generalized Graph Differences

Abstract

Formulating the multi object tracking problem as a network flow optimization problem is a popular choice. In this paper an efficient way of learning the weights of such a network is presented. It separates the problem into one embedding of feasible solutions into a one dimensional feature space and one optimization problem. The embedding can be learned using standard SGD type optimization without relying on an additional optimizations within each step. Training data is produced by performing small perturbations of ground truth tracks and representing them using generalized graph differences, which is an efficient way introduced to represent the difference between two graphs. The proposed method is evaluated on DukeMTMCT with competitive results.

Deep Learning for UAV-based Object Detection and Tracking: A Survey

“…Shuai et al [178] proposed MOT [150] Orientation, scale UAVDT Remote Sens. 2019 -Flow-tracker [151] ID Switches, error detection VisDrone-MOT ICCV 2019 -TNT [152] Camera motion, occlusion, pose variation VisDrone-MOT, Own ACM-MM 2019 -HMTT [153] Target motion, shape, appearance changes VisDrone-MOT ICCV 2019 -Yang et al [154] Target position changes Own RS 2019 https://frank804.github.io/ GGD [155] False alarms, missed detections VisDrone-MOT ICCV 2019 https://github.com/hakanardo/ggdtrack COMET [156] Small object UAVDT, VisDrone-MOT, Small-90 ICCV 2019 -Self-balance [157] Appearance, motion UAVDT Multimedia Asia 2019 -Abughalieh et al [117] Low detailed targets DARPA, VIVID, Own Multimed. Tools.Appl.…”

“…Besides the aforementioned methods, other methods for multiple object tracking are also available, such as generalized graph differences (GGD) for network flow optimization [155] with an efficient representation of differences between graphs, context-aware IoU-guided tracker (COMET) [156] with offline proposal generation and multitask two-stream network. There is also literature focusing on designing MOT patrol [149] or mobile [117] systems for UAV video.…”

“…OSIM [150] YOLOv3+Kalman filtering 2720 × 1530 Workstation(NVIDIA GeForce GTX 1080 Ti/-) 30 2019 +deep appearance feature Workstation(Intel UHD Graphics 630/-) Self-balance [157] LSTM 1080 × 540 Workstation(NVIDIA Titan X/32GB) -2019 Flow-tracker [151] Optical Flownet+IOU -Workstation(NVIDIA GTX 1080Ti/-) 5 2019 HMTT [153] CenterNet+IOU+OSNet ---2019 Yang et al [154] YOLOv3 + dense-trajectory-Voting 1920 × 1080 Workstation(NVIDIA GeForce GTX1080Ti/6GB) 8.6 2019 GGD [155] Faster RCNN+GGD -Workstation(NVIDIA GTX 1080/-) -2019 COMET [156] ResNet-50+Two-stream network 1080 × 540 Workstation(NVIDIA Tesla V100/16GB) 24 2019 Abughalieh et al [117] FAST 320 × 240 Laptop(Core i7-2670QM/6GB) 26.3 2019 Abughalieh et al [117] FAST 320 × 240 Embedded(Raspberry Pi 2/1GB) 10.8 2019 IPGAT [159] SiameseNet+LSTM+CGAN -Workstation(NVIDIA Titan X/32GB) -2020 Kapania et al [160] YOLOv3+RetinaNet ---2020 PAS tracker [161] Cascade R-CNN+Similarity ---2020 DAN [77] RetinaNet+DeepSORT 1500 × 1000 -2020 DQN [162] Faster R-CNN ---2021 Youssef et al [80] Cascade visible-thermal infrared cameras equipped with drones. In label terms, the bounding box is not limited to horizontal bounding box strongly dependent on robustness to direction, even have oriented bounding box, e.g., in the Vehicle Dataset.…”