A real-time computer vision system for measuring traffic parameters

Beymer, David; McLauchlan, Philip F.; Coifman, Benjamin; Malik, Jitendra

doi:10.1109/cvpr.1997.609371

Cited by 361 publications

(214 citation statements)

References 8 publications

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“…Most extant approaches to classifying vehicular traffic videos use a combination of segmentation and tracking, e.g., [24,25,3,28,35,11,23,22,30,29,6]. The general procedure consists of the following three steps: (i) motion detection, (ii) tracking of the individual vehicles and (iii) combining trajectory information to derive an overall description of traffic flow.…”

Section: Related Workmentioning

confidence: 99%

Classification of traffic video based on a spatiotemporal orientation analysis

Derpanis

Wildes

2011

2011 IEEE Workshop on Applications of Computer Vision (WACV)

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This paper describes a system for classifying traffic congestion videos based on their observed visual dynamics. Central to the proposed system is treating traffic flow identification as an instance of dynamic texture classification. More specifically, a recent discriminative model of dynamic textures is adapted for the special case of traffic flows. This approach avoids the need for segmentation, tracking and motion estimation that typify extant approaches. Classification is based on matching distributions (or histograms) of spacetime orientation structure. Empirical evaluation on a publicly available data set shows high classification performance and robustness to typical environmental conditions (e.g., variable lighting).

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Section: Related Workmentioning

confidence: 99%

Classification of traffic video based on a spatiotemporal orientation analysis

Derpanis

Wildes

2011

2011 IEEE Workshop on Applications of Computer Vision (WACV)

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“…Penelitian yang relevan dengan makalah ini adalah dalam sistem pengendali cerdas lalu lintas dalam menggunakan background estimation untuk memfilter setiap kendaraan dari background dan dengan menggunakan logika fuzzy untuk mengendalikan lampu lalu lintas [2]. Sebuah sistem real time menggunakan metode berbasis fitur bersama dengan oklusi digunakan untuk pelacakan kendaraan dalam kondisi lalu lintas padat [3]. Sebuah pengenalan metode obyek bergerak menggunakan blob analysis untuk memperbaiki model background [4].…”

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Kendali Lampu Lalu Lintas dengan Deteksi Kendaraan Menggunakan Metode Blob Detection

Hidayati¹

2017

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Abstract-Traffic jam is a major traffic problem often found in big cities of Indonesia. It is because the number of vehicles increases annually. Therefore, a simulation to detect the number of vehicles in every lane of traffic is needed to monitor the traffic. Traffic control is also required in order to reduce traffic jam. This paper develops a vehicle detection and counting system using image processing. Detection is carried out using image segmentation which is processed by object filtering and blob extraction. Morphological operators are employed for blob extraction. Testing is conducted using a video obtained from the ATCS Bandung. The video is taken at the Laswi -A. Yani intersection Bandung. Software prototypes are created in C++, using Windows Forms Application as a programming library for Windows and Open CV for image processing module. The result shows that blob detection method can give good results if there is no intersection between blobs of each car. The performance is poor when this method is used for heavy traffic conditions, where the cars are close to each other. The performance level of sensitivity is 91.67%, precision is 61.11%, specificity is 80.55%, f-Measure is 73.33%, and accuracy is 83.33%. The accuracy for vehicles detection on sunny condition is 82.11% and reduced by 76.50% on rainy condition. This method works better in quiet condition, with accuracy of 83.07%, and is reduced by 67.70% in crowded condition. The average processing time is 0.042 seconds when using video, and 0.033 seconds using real camera. Kata kunci--deteksi kendaraan, pengolahan citra digital, blob detection, morfologi, OpenCV I. PENDAHULUAN Pada setiap tahun, peningkatan jumlah kendaraan semakin bertambah. Hal ini berakibat munculnya masalah lalu lintas kendaraan, misalnya kemacetan lalu lintas, kecelakaan lalu lintas, polusi udara, dan sebagainya. Kemacetan lalu lintas telah menjadi masalah signifikan. Beberapa hal yang telah dilakukan untuk menangani masalah kemacetan misalnya awal infrastruktur transportasi, lebar trotoar, dan pelebaran jalan. Namun, hal ini belum mampu mengurangi kemacetan kota, sehingga banyak peneliti tertarik melakukan penelitian terhadap sistem transportasi cerdas (intelligent transportation systems, ITS), seperti memprediksi arus lalu lintas pada pemantauan kegiatan di persimpangan lalu lintas untuk mendeteksi kemacetan. Dalam hal ini diperlukan peningkatan pengawasan lalu lintas pada deteksi kendaraan. Deteksi kendaraan secara otomatis dalam data video surveillance merupakah bagian dari computer vision. Intisari-Deteksi kendaraan dan penghitungan kendaraan adalah hal penting dalam komputasi kemacetan lalu lintas di jalan raya. Sistem yang diusulkan di sini bertujuan untuk memberikan data jumlah kendaraan pada persimpangan jalan raya. Tujuan utama makalah ini adalah untuk mengembangkan metode deteksi kendaraan secara otomatis dan menghitung kendaraan pada simpang empat lalu lintas. Sebuah sistem diimplementasikan untuk mendeteksi dan menghitung objek secara dinamis dan statis. Pemantauan ja...

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“…There is however a lack of contribution to establish a set of standard services that can be used by a lane coordinator to retrieve car plates when the DSRC system could not set OBU identifier or there was some problem with the customer bank account. The vision systems have a crescent importance as flexible sensor/measurement systems considering that they can be deployed without a direct interference with the observed entities (Beymer, 1997), (Abrantes, 2002). In the case of a motorway lane, a camera can be installed somewhere in a motorway lane and used to acquire traffic snapshots or video streams, which are later analyzed to infer relevant features.…”

Section: Image Acquisition and Feature Recognition Servicesmentioning

confidence: 99%