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TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking

2021-01-11 21:52:25
Akshay Rangesh, Pranav Maheshwari, Mez Gebre, Siddhesh Mhatre, Vahid Ramezani, Mohan M. Trivedi


tract: This study follows many previous approaches to multi-object tracking (MOT) that model the problem using graph-based data structures, and adapts this formulation to make it amenable to modern neural networks. Our main contributions in this work are the creation of a framework based on dynamic undirected graphs that represent the data association problem over multiple timesteps, and a message passing graph neural network (GNN) that operates on these graphs to produce the desired likelihood for every association therein. We further provide solutions and propositions for the computational problems that need to be addressed to create a memory-efficient, real-time, online algorithm that can reason over multiple timesteps, correct previous mistakes, update beliefs, possess long-term memory, and handle missed/false detections. In addition to this, our framework provides flexibility in the choice of temporal window sizes to operate on and the losses used for training. In essence, this study provides a framework for any kind of graph based neural network to be trained using conventional techniques from supervised learning, and then use these trained models to infer on new sequences in an online, real-time, computationally tractable manner. To demonstrate the efficacy and robustness of our approach, we only use the 2D box location and object category to construct the descriptor for each object instance. Despite this, our model performs on par with state-of-the-art approaches that make use of multiple hand-crafted and/or learned features. Experiments, qualitative examples and competitive results on popular MOT benchmarks for autonomous driving demonstrate the promise and uniqueness of the proposed approach.

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3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot