A light tracker for online multiple pedestrian tracking

Wang, Nan; Zou, Qi; Ma, Qiulin; Huang, Yaping; Luan, Di

doi:10.1007/s11554-020-00962-3

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…Furthermore, we also discussed what type of pattern extraction strategy they have used for visual feature extraction. For instance, Wang et al [10] proposed a real-time multiple pedestrian tracker for movies captured from both stationary and moving cameras in an area with no restrictions. In a similar method, Shi et al [11] created a CNN-based model capable of analyzing congested scenarios in real-time.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

Zahir¹,

Khan²,

Ullah³

et al. 2023

Computer Systems Science and Engineering

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The analysis of overcrowded areas is essential for flow monitoring, assembly control, and security. Crowd counting's primary goal is to calculate the population in a given region, which requires real-time analysis of congested scenes for prompt reactionary actions. The crowd is always unexpected, and the benchmarked available datasets have a lot of variation, which limits the trained models' performance on unseen test data. In this paper, we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene. The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization (EvoNorm). This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples. The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters, which enables real-time processing and solves the density drift problem due to its large receptive field. Five benchmark datasets are used in this study to assess the proposed model, resulting in the conclusion that it outperforms conventional models.

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Section: Literature Reviewmentioning

confidence: 99%

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

Zahir¹,

Khan²,

Ullah³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…All this should involve a computational cost consistent with the needs of automotive applications. For instance, in [36] the authors propose a lightweight solution where the feature extractor consists in only two convolutional layers, while a careful selection of motion patterns solves the data association problem.…”

Section: Ensuring Temporal Coherencementioning

confidence: 99%

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

2021

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This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

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An online multiple object tracker based on structure keeper net

Wang

Zou

et al. 2021

Appl Intell

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A light tracker for online multiple pedestrian tracking

Cited by 4 publications

References 57 publications

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

An online multiple object tracker based on structure keeper net

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