Modular Multi Target Tracking Using LSTM Networks

• URL: http://arxiv.org/abs/2011.09839v1
• Date: Mon, 16 Nov 2020 15:58:49 GMT
• Title: Modular Multi Target Tracking Using LSTM Networks
• Authors: Rishabh Verma, R Rajesh and MS Easwaran
• Abstract summary: This paper proposes a model free end-to-end approach for airborne target tracking system using sensor measurements. The proposed modular blocks can be independently trained and used in multitude of tracking applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The process of association and tracking of sensor detections is a key element in providing situational awareness. When the targets in the scenario are dense and exhibit high maneuverability, Multi-Target Tracking (MTT) becomes a challenging task. The conventional techniques to solve such NP-hard combinatorial optimization problem involves multiple complex models and requires tedious tuning of parameters, failing to provide an acceptable performance within the computational constraints. This paper proposes a model free end-to-end approach for airborne target tracking system using sensor measurements, integrating all the key elements of multi target tracking -- association, prediction and filtering using deep learning with memory. The challenging task of association is performed using the Bi-Directional Long short-term memory (LSTM) whereas filtering and prediction are done using LSTM models. The proposed modular blocks can be independently trained and used in multitude of tracking applications including non co-operative (e.g., radar) and co-operative sensors (e.g., AIS, IFF, ADS-B). Such modular blocks also enhances the interpretability of the deep learning application. It is shown that performance of the proposed technique outperforms conventional state of the art technique Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) filter.

Related papers

• Is Multiple Object Tracking a Matter of Specialization? [33.59920084936913]
  Training end-to-end transformer-based trackers in heterogeneous scenarios poses significant challenges. We introduce. The Scenario-specific Tracking Architecture (PASTA), a novel framework that combines. Efficient Fine-Tuning (PEFT) and Modular Deep Learning.
  arXiv  Detail & Related papers  (2024-11-01T13:03:58Z)
• DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing [8.530409994516619]
  Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies. We propose Disparity-guided Multispectral Mamba (DMM), a framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task.
  arXiv  Detail & Related papers  (2024-07-11T02:09:59Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML [4.2019872499238256]
  We propose a novel strategy for deploying Deep Neural Networks on microcontrollers (TinyML) based on Multi-Objective Bayesian optimization (MOBOpt) Our methodology aims at efficiently finding tradeoffs between a DNN's predictive accuracy, memory consumption on a given target system, and computational complexity.
  arXiv  Detail & Related papers  (2023-05-23T14:31:52Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)
• Transformer-based assignment decision network for multiple object tracking [0.0]
  We introduce Transformer-based Assignment Decision Network (TADN) that tackles data association without the need of explicit optimization during inference. Our proposed approach outperforms the state-of-the-art in most evaluation metrics despite its simple nature as a tracker.
  arXiv  Detail & Related papers  (2022-08-06T19:47:32Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Object Tracking through Residual and Dense LSTMs [67.98948222599849]
  Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative. DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
  arXiv  Detail & Related papers  (2020-06-22T08:20:17Z)
• A Unified Object Motion and Affinity Model for Online Multi-Object Tracking [127.5229859255719]
  We propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA. UMA integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. We equip our model with a task-specific attention module, which is used to boost task-aware feature learning.
  arXiv  Detail & Related papers  (2020-03-25T09:36:43Z)

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.