JAIST Repository
https://dspace.jaist.ac.jp/Title

交通監視システムのための密度を意識した注意ネット
ワークを用いた車両密度推定

Author(s)SOOKSATRA, SornCitation
Issue Date

2021‑03

TypeThesis or Dissertation

Text version

ETDURLhttp://hdl.handle.net/10119/17478

RightsDescription

Supervisor:吉高淳夫, 先端科学技術研究科, 博士

Japan Advanced Institute of Science and Technology

Vehicle Density Estimation Using Density-Aware Attention Network for Traffic Surveillance
SystemThe surveillance system is widely deployed in several applications for observing thecharacteristic of target density because it has an advantage in low installation and maintenancecosts. In a traffic surveillance system, this characteristic is usually applied in a traffic light control
system (TLCS) from the number of vehicles. It helps to control the period of traffic lights andprevent any traffic accidents from vehicles. Besides, the traffic characteristic is useful information
for road users to avoid crowded regions, including vehicles, people, and others. Since this systemutilizes computer vision-based techniques, it is sensitive to outdoor environments (e.g., lighting
conditions, traffic viewpoints, and so on). In addition, hardware with low performance usually isdeployed in the surveillance system. It means that the system should be compact and requires low
computational cost for operation. Therefore, density estimation cannot be operated properly under
any circumstances without concerning these conditions.
Recently, there are several related studies on density estimation. It is well known thatdensity is originally estimated by detecting and counting their regions in the input image. The
counting target regions were classified by their visual features (appearance and motion features).
Instead of relying on a detection-based approach, recent studies focused on end-to-end learning
methods relied on regression-based approaches. These approaches concern holistic features which
are visual features extracted from whole input images predicted by a prediction model. The holisticfeatures are utilized for mapping dense regions into density maps which represent the change ofobject density, where their ground truth was prepared by a convolution between Gaussian
distribution and target coordinates corresponding to the target sizes. Nowadays, the task of vehicle
counting via a regression-based approach is riddled with a scale-aware model to handle scaling
problems. Prediction models are usually designed by several stacked CNNs in parallel, which arecalled multi-column network architectures. In short, one stack of CNN is used to predict density
maps on a specific size of the vehicle. However, these techniques have problems related topractical application as follows:••

With the variation of camera viewpoints, the target sizes can be calculated accurately
resulting in the misclassification for density map formulation in the ground truth of an
estimation network.
Recent regression models with multi-column network architectures have a large number ofmodel parameters and high computational costs using several network architectures forvarious target sizes.

The purpose of this study is to examine the only one stack of CNN, which is called a singlecolumn network architecture, and reduce computational costs and model parameters while keeping
similar counting accuracy to multi-column network architectures. It also should not suffer from ascaling problem by avoiding the utilization of target sizes. It found that the target size can becategorized by their local densities which are related to distances of their neighbor target for
preparing the ground truth. The proposed prediction model chooses to investigate the connections

between feature maps from different layers, where holistic features of small and large objects canbe extracted from feature maps in shallow and deep layers, respectively. The connection can bedone by skip connections to integrate feature maps from shallow layers with another in deeperlayers. This process, which is called forward connections, can recover the holistic features of smallobjects. On the other hand, the backward connections are introduced by extracting feature maps
from deep layers to combine with the shallower layer. It can be expected that information on a
deep layer can help to optimize the shallow layer, where the performance in an earlier stage canbe improved. Considering the quality of a density map, feature maps in every layer should havethe same resolution to prevent information loss from adjusting their resolutions. Then, poolinglayers are replaced with a dilated convolutional layer. Therefore, the contributions of thisdissertation can be summarized as follows:••

Instead of relying on the target size, the proposed density map utilizes average distancesamong target samples where it is designed for visualizing the difference pattern of various
vehicle densities (high and low density regions).
To reduce computation cost, a single column network architecture for vehicle density
estimation is designed by including skip connections and dilated convolutions to extract
holistic features from intermediate convolutional layers and keep semantic information for
density map estimation, respectively.

Since vehicle density estimation is mainly focused on this research, all models areevaluated by the common criteria for vehicle density accuracy. The state-of-the-art of regressionbased approach was implemented for comparison. In addition, well-known CNNs with skip
connections (e.g., U-net, Resnet, and Densenet) were also applied for analysis. The empiricalresults show that the proposed prediction model with backward connections achieved a vehicle
density accuracy 92.47 % which is close to accuracy of state-of-the-art (93.33 %). From this result,the achievement is summarized as follows:••

In the density map configuration, the target size estimated by an average distance of thetarget is insensitive to camera viewpoints.
From the point of view of counting accuracy, the proposed method with a single-column
network achieves a promising result which is close to the vehicle counting accuracy froma multi-column network or network with a large number of model parameters.

Moreover, the proposed network satisfies with the minimum requirement of TLCS and itis effective to reduce under-counting errors. However, there is a room for improvement in otherdatasets consisting of overlapping target regions or crowd counting datasets.

Keyword: surveillance system, vehicle density estimation, regression model, skip connection,
dilated convolution, traffic analysis.