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http://dx.doi.org/10.9717/kmms.2020.23.10.1236

LSTM Network with Tracking Association for Multi-Object Tracking  

Farhodov, Xurshedjon (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Moon, Kwang-Seok (Dept. of Electronics Engineering, Pukyong National University)
Lee, Suk-Hwan (Dept. of Computer Engineering, Donga University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Publication Information
Abstract
In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.
Keywords
Deep Learning; Object Tracking; LSTM Network; RNN; Object Detection; Multi-Object Tracking; MOT; CNN; Keras; Dense Layer; Neural Network;
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Times Cited By KSCI : 2  (Citation Analysis)
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