Enhancing the Association in Multi-Object Tracking via Neighbor Graph

• URL: http://arxiv.org/abs/2007.00265v1
• Date: Wed, 1 Jul 2020 06:21:31 GMT
• Title: Enhancing the Association in Multi-Object Tracking via Neighbor Graph
• Authors: Tianyi Liang, Long Lan, Zhigang Luo
• Abstract summary: We propose to handle the problem via making full use of the neighboring information. Our motivations derive from the observations that people tend to move in a group. We first utilize the modern-temporal relations produced by the tracking self to efficiently select suitable neighbors for the targets.
• Score: 11.13923339111758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most modern multi-object tracking (MOT) systems follow the tracking-by-detection paradigm. It first localizes the objects of interest, then extracting their individual appearance features to make data association. The individual features, however, are susceptible to the negative effects as occlusions, illumination variations and inaccurate detections, thus resulting in the mismatch in the association inference. In this work, we propose to handle this problem via making full use of the neighboring information. Our motivations derive from the observations that people tend to move in a group. As such, when an individual target's appearance is seriously changed, we can still identify it with the help of its neighbors. To this end, we first utilize the spatio-temporal relations produced by the tracking self to efficiently select suitable neighbors for the targets. Subsequently, we construct neighbor graph of the target and neighbors then employ the graph convolution networks (GCN) to learn the graph features. To the best of our knowledge, it is the first time to exploit neighbor cues via GCN in MOT. Finally, we test our approach on the MOT benchmarks and achieve state-of-the-art performance in online tracking.

Related papers

• High Pileup Particle Tracking with Object Condensation [7.962871190916326]
  Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking. We consider an alternative based on object condensation (OC), a multi-objective learning framework designed to cluster points (hits) belonging to an arbitrary number of objects (tracks) and regress the properties of each object.
  arXiv  Detail & Related papers  (2023-12-06T19:00:00Z)
• NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets [22.877838749427674]
  NeighborTrack is a post-processor that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results.
  arXiv  Detail & Related papers  (2022-11-12T13:43:58Z)
• Improving Fraud detection via Hierarchical Attention-based Graph Neural Network [6.7713383844867385]
  Graph Neural Network (HA-GNN) for fraud detection incorporates weighted adjacency matrices across different relations against camouflage. We generate node embeddings by aggregating information from local/long-range structures and original node features. Experiments on three real-world datasets demonstrate the effectiveness of our model over the state-of-the-arts.
  arXiv  Detail & Related papers  (2022-02-12T16:27:16Z)
• Dynamic Sequential Graph Learning for Click-Through Rate Prediction [29.756257920214168]
  We propose a novel method to enhance users' representations by utilizing collaborative information from the local sub-graphs associated with users or items. Results on real-world CTR prediction benchmarks demonstrate the improvements brought by DSGL.
  arXiv  Detail & Related papers  (2021-09-26T09:23:43Z)
• Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [58.30147362745852]
  Data association across frames is at the core of Multiple Object Tracking (MOT) task. Existing methods mostly ignore the context information among tracklets and intra-frame detections. We propose a novel learnable graph matching method to address these issues.
  arXiv  Detail & Related papers  (2021-03-30T08:58:45Z)
• Learning to Track with Object Permanence [61.36492084090744]
  We introduce an end-to-end trainable approach for joint object detection and tracking. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
  arXiv  Detail & Related papers  (2021-03-26T04:43:04Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking [80.02322563402758]
  One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets. We introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion.
  arXiv  Detail & Related papers  (2020-04-16T06:43:11Z)
• GPS-Net: Graph Property Sensing Network for Scene Graph Generation [91.60326359082408]
  Scene graph generation (SGG) aims to detect objects in an image along with their pairwise relationships. GPS-Net fully explores three properties for SGG: edge direction information, the difference in priority between nodes, and the long-tailed distribution of relationships. GPS-Net achieves state-of-the-art performance on three popular databases: VG, OI, and VRD by significant gains under various settings and metrics.
  arXiv  Detail & Related papers  (2020-03-29T07:22:31Z)

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.