A reliable counting vehicles method in traffic flow monitoring

Jun, Song; Bai, A-Ning; Ren, Xue

doi:10.1109/cisp.2011.6099921

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Orderly self-organising traffic flow is only possible if there is adequate information on the arrival and departure of vehicles, vehicle detection technology, and counting of arrivals at the intersections. Several methods are available for this purpose, including sensors based on light detection, cameras, areal sensing, or conventional ground detectors using magnetic sensors [26,27,28,29,30]. Once an adequate operational sensing method is developed, various artificial intelligence and adaptive techniques can be employed [31,32].…”

Section: System Implementation and Resultsmentioning

confidence: 99%

“…Wireless networks have been used in urban traffic control extensively and many methods have been reported. The cellular digital packet data (CDPD), and the IEEE 802.11-based wireless systems are some examples [5,6,7,8,26,27] on the applications of wireless techniques. In this paper, the ZigBee wireless communication technology has been selected, since it allows the communications of PLCs and SCADA system effectively and efficiently.…”

Section: Laboratory Setup Of Traffic Flow and Wireless Systemmentioning

confidence: 99%

See 1 more Smart Citation

Instrumentation for safe vehicular flow in intelligent traffic control systems using wireless networks

Eren

Pakka

AlGhamdi

et al. 2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

This paper describes a ZigBee based wireless system to assists traffic flow on arterial urban roads. Real-time simulation in laboratory environment is conducted to determine the traffic throughput to avoid possible congestions or ease existing congestions. Random numbers are generated to mimic approaching traffic, and this information is shared by a ZigBeebased real-time wirelessly network. Wireless nodes are connected to different PLCs representing different traffic lights in a cluster. Once the information is shared the timing and sequencing decisions are taken collectively in a synchronized manner. In this paper, the information is displayed on SCADA connected to each PLC for viewing the characteristics of continuous vehicular flow. It is found that the topology of the network can play an important role in the throughput of data, which may be critical in safety critical operations such as the control of traffic lights. This paper aims to highlight some of the possible effects of dataflow flow and time-delays faced by modern intelligent control of traffic lights.

show abstract

Section: System Implementation and Resultsmentioning

confidence: 99%

Section: Laboratory Setup Of Traffic Flow and Wireless Systemmentioning

confidence: 99%

Instrumentation for safe vehicular flow in intelligent traffic control systems using wireless networks

Eren

Pakka

AlGhamdi

et al. 2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

show abstract

An accurate vehicle counting approach based on block background modeling and updating

Song

Wang

2014

2014 7th International Congress on Image and Signal Processing

View full text Add to dashboard Cite

An Intelligent Traffic Analysis and Prediction System Using Deep Learning Technique

Sasikala¹,

Neelaveni²,

Jose³

2024

Int. J. Artif. Intell. Tools

View full text Add to dashboard Cite

Accurate identification of vehicles and estimating density in traffic surveillance systems is a challenging task, particularly in scenarios with closely spaced lanes. Single Shot MultiBox Detector (SSD) is introduced in vehicle detection and classification due to its speed and accuracy. It utilizes a transfer learning technique that enables them to utilize features from pretrained Convolutional Neural Networks (CNNs). Although the utilization of multi-scale feature maps in SSD has achieved efficient results in vehicle detection, it struggles to identify key features of vehicles due to its one-stage detection approach. Furthermore, the use of VGG16 as the backbone network in these approaches leads to the loss of fine-grained details, posing challenges in accurately localizing and classifying small vehicles. Also, there is no interaction between high- and low-level features, which restricts the networks ability to effectively integrate and utilize both features for accurate vehicle detection. To overcome these limitations, an improved SSD approach is introduced in this paper, leveraging interactive multi-scale attention characteristics to accurately detect and classify vehicles. This approach utilizes ResNet50 as the network backbone to overcome the limitations of traditional SSDs in detecting small vehicles. Also, an attention block is incorporated into it to focus on key details and assign higher attention to relevant pixels within the feature map. Also, the network employs a parallel detection framework and shares multi-scale layers (both high and low), enabling efficient detection of vehicles with various sizes. Then, traffic density is estimated based on the weights assigned to the categorized vehicles obtained from the improved SSD and the recorded area. Finally, traffic is classified as high, low, or moderate by comparing the estimated density to a threshold value. By adding attention characteristics of different scales to the original detection branch and replacing the VGG16 with ResNet50 of the SSD technique using our method, the feature representation capability and detection accuracy are both significantly improved.

show abstract

A reliable counting vehicles method in traffic flow monitoring

Cited by 4 publications

References 3 publications

Instrumentation for safe vehicular flow in intelligent traffic control systems using wireless networks

Instrumentation for safe vehicular flow in intelligent traffic control systems using wireless networks

An accurate vehicle counting approach based on block background modeling and updating

An Intelligent Traffic Analysis and Prediction System Using Deep Learning Technique

Contact Info

Product

Resources

About