Engineering an Efficient Object Tracker for Non-Linear Motion

• URL: http://arxiv.org/abs/2407.00738v1
• Date: Sun, 30 Jun 2024 15:50:54 GMT
• Title: Engineering an Efficient Object Tracker for Non-Linear Motion
• Authors: Momir Adžemović, Predrag Tadić, Andrija Petrović, Mladen Nikolić,
• Abstract summary: The goal of multi-object tracking is to detect and track all objects in a scene while maintaining unique identifiers for each. This task is especially hard in case of scenarios involving dynamic and non-linear motion patterns. In this paper, we introduce DeepMoveSORT, a novel, carefully engineered multi-object tracker designed specifically for such scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of multi-object tracking is to detect and track all objects in a scene while maintaining unique identifiers for each, by associating their bounding boxes across video frames. This association relies on matching motion and appearance patterns of detected objects. This task is especially hard in case of scenarios involving dynamic and non-linear motion patterns. In this paper, we introduce DeepMoveSORT, a novel, carefully engineered multi-object tracker designed specifically for such scenarios. In addition to standard methods of appearance-based association, we improve motion-based association by employing deep learnable filters (instead of the most commonly used Kalman filter) and a rich set of newly proposed heuristics. Our improvements to motion-based association methods are severalfold. First, we propose a new transformer-based filter architecture, TransFilter, which uses an object's motion history for both motion prediction and noise filtering. We further enhance the filter's performance by careful handling of its motion history and accounting for camera motion. Second, we propose a set of heuristics that exploit cues from the position, shape, and confidence of detected bounding boxes to improve association performance. Our experimental evaluation demonstrates that DeepMoveSORT outperforms existing trackers in scenarios featuring non-linear motion, surpassing state-of-the-art results on three such datasets. We also perform a thorough ablation study to evaluate the contributions of different tracker components which we proposed. Based on our study, we conclude that using a learnable filter instead of the Kalman filter, along with appearance-based association is key to achieving strong general tracking performance.

Related papers

• Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring [71.60457491155451]
  Eliminating image blur produced by various kinds of motion has been a challenging problem. We propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative Filter. Our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-04-19T19:44:24Z)
• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking [3.5693768338940304]
  We propose two innovative data-driven filtering methods for tracking-by-detection systems. The first method employs a Bayesian filter with a trainable motion model to predict an object's future location. The second method, an end-to-end trainable filter, goes a step further by learning to correct detector errors.
  arXiv  Detail & Related papers  (2024-02-15T10:47:44Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports [26.33239898091364]
  We propose a novel online and robust multi-object tracking approach named deep ExpansionIoU (Deep-EIoU) for sports scenarios. Unlike conventional methods, we abandon the use of the Kalman filter and leverage the iterative scale-up ExpansionIoU and deep features for robust tracking in sports scenarios. Our proposed method demonstrates remarkable effectiveness in tracking irregular motion objects, achieving a score of 77.2% on the SportsMOT dataset and 85.4% on the SoccerNet-Tracking dataset.
  arXiv  Detail & Related papers  (2023-06-22T17:47:08Z)
• Interaction-Aware Labeled Multi-Bernoulli Filter [5.255783459833821]
  We present a novel approach to incorporate target interactions within the prediction step of an RFS-based multi-target filter. The method has been developed for two practical applications of tracking a coordinated swarm and vehicles.
  arXiv  Detail & Related papers  (2022-04-19T04:23:32Z)
• DSRRTracker: Dynamic Search Region Refinement for Attention-based Siamese Multi-Object Tracking [13.104037155691644]
  We propose an end-to-end MOT method, with a Gaussian filter-inspired dynamic search region refinement module. Our method can achieve the state-of-the-art performance with reasonable speed.
  arXiv  Detail & Related papers  (2022-03-21T04:14:06Z)
• Implicit Motion Handling for Video Camouflaged Object Detection [60.98467179649398]
  We propose a new video camouflaged object detection (VCOD) framework. It can exploit both short-term and long-term temporal consistency to detect camouflaged objects from video frames.
  arXiv  Detail & Related papers  (2022-03-14T17:55:41Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• Learning to Segment Rigid Motions from Two Frames [72.14906744113125]
  We propose a modular network, motivated by a geometric analysis of what independent object motions can be recovered from an egomotion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel.
  arXiv  Detail & Related papers  (2021-01-11T04:20:30Z)

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.