Traffic Analytics With Low-Frame-Rate Videos

Luo, Zhiming; Jodoin, Pierre-Marc; Su, Songzhi; Li, Shaozi; Larochelle, Hugo

doi:10.1109/tcsvt.2016.2632439

Cited by 27 publications

(54 citation statements)

References 40 publications

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“…In Section VI we have compared our experimental setup and achieved results with the ones mentioned in [8]. In another extended paper published by Luo et al [14], the researchers have used SegCNN and RegCNN to analyze and classify traffic. In both the aforementioned papers the authors are training and classifying traffic images after the video frames are transferred to the server from the interconnected camera network.…”

Section: Related Workmentioning

confidence: 98%

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MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

Dey

Kalliatakis

Saha

et al. 2018

2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)

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Intelligent Transportation Systems (ITS) have become an important pillar in modern "smart city" framework which demands intelligent involvement of machines. Traffic load recognition can be categorized as an important and challenging issue for such systems. Recently, Convolutional Neural Network (CNN) models have drawn considerable amount of interest in many areas such as weather classification, human rights violation detection through images, due to its accurate prediction capabilities. This work tackles real-life traffic load recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin it as MAT-CNN-SOPC, which uses an intelligent retraining mechanism of the CNN with known environments. The proposed methodology is capable of enhancing the efficacy of the approach by 2.44x in comparison to the state-of-art and proven through experimental analysis. We have also introduced a mathematical equation, which is capable of quantifying the suitability of using different CNN models over the other for a particular application based implementation.Index Terms-Convolutional neural network (CNN), traffic analysis, traffic density, transfer learning, system-on-aprogrammable-chip (SOPC).

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Section: Related Workmentioning

confidence: 98%

“…978-1-5386-7753-7/18/$31.00 2018 IEEE bandwidth of the network [8], [14]. Moreover due to cost constraint of such interconnected camera networks and associated servers, many developing countries might not be able to adopt and implement such sophisticated state-of-the-art traffic analysis and categorization methodologies.…”

Section: Introductionmentioning

confidence: 99%

MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

Dey

Kalliatakis

Saha

et al. 2018

2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)

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“…In recent times, there has also been emergence of several methods capable of monitoring and analyzing traffic using motionless analysis of videos [4], [9], [20], where videos of traffic are broken into frames instead, and the frames are analyzed for further computation or prediction. The main motivation to utilize methodologies consisting of motionless analysis of video is that it is difficult to stream high-frame rate videos gathered by a large network of interconnected cameras due to bandwidth limitation.…”

Section: Introductionmentioning

confidence: 99%

“…Although motionless analysis of videos have their own benefits, they also come with limitations described with the following observations. Observation 1: Although several implementations of such methods were able to achieve high prediction accuracy on known dataset [4], [9], [20], but in some test cases the analysis were not accurate at all. The reason for failed prediction/analysis is that in some cases it is difficult to predict the label of an image frame from a video if the ground truth of the image is overlapping with several other categories (labels) instead of falling under one category.…”

Section: Introductionmentioning

confidence: 99%

“…Although there have been some recent studies, which focused on future predictions of motion in ego-centric videos [21], [22], such as predicting the future position of a person based on the current image frame, but no study to our best knowledge have tried to predict the scene 3 [23], [24] of a video from image frames taking predictions from immediate previous frames into account to provide a more holistic analysis over a time period. Hence, we call such an analysis as Temporal Motionless Analysis of Video (TMAV) and several target applications of CNN such as traffic categorization require such kind of analysis in comparison with traditional one [4], [9], [25]. Observation 2: In the study [9], Dey et al proposed a methodology to implement a CNN which is trained on a configurable 1 Ground truth of the image frame in this case is the information gained through empirical evidence as opposed to the inference made by the CNN model.…”

Section: Introductionmentioning

confidence: 99%

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TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC

Dey

Singh²,

Prasad³

et al. 2019

Preprint

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<div><div><div><p>Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs.</p></div></div></div>

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Vision‐based vehicle detection for road traffic congestion classification

Chetouane

Mabrouk

Jemili

et al. 2020

Concurrency and Computation

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SummaryDue to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.

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Traffic Analytics With Low-Frame-Rate Videos

Cited by 27 publications

References 40 publications

MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC

Vision‐based vehicle detection for road traffic congestion classification

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