Application of Image Processing and Three-Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the three-dimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods

DETEKSI MOBIL MENGGUNAKAN OPERASI MORFOLOGI DAN KLASIFIKASI ADABOOST

Pendeteksian mobil memiliki peranan penting dalam manajemen sistem lalu lintas yang dinamis. Hal ini sangat berkaitan dengan penanganan kemacetan di kota-kota besar. Agar penangangan manajemen lalu lintas kendaraan ini bisa optimal, tentu sangat berpengaruh dari informasi yang didapatkan dari sistem pendeteksi kendaraan ini. Hal ini yang menjadi tantangan dalam pendeteksian mobil pada jalan raya maupun jalan bebas hambatan. Banyak metode yang dapat dilakukan untuk melakukan pendeteksian mobil, mulai dari menggunakan sensor hingga penggunaan kamera CCTV. Sebagai alat yang multifungsi dan lebih murah, pemerintah cenderung menerapkan kamera CCTV sebagai alat yang digunakan untuk memantau mobil di jalan raya maupun di jalan bebas hambatan. Oleh karena itu, diperlukan sebuah sistem deteksi mobil yang menggunakan citra CCTV yang akurat dan mempunyai proses yang cepat. Metode yang yang digunakan pada tugas akhir ini yaitu melakukan pendeteksian mobil dengan menggunakan dua layer pada tahap pendeteksiannya. Yaitu pada layer pertama yang akan dilakukan operasi morfologi untuk menemukan kemungkinan lokasi lampu depan mobil. Pada layer ini dilakukan operasi Top Hat Transform untuk mendapatkan citra yang kontras, dan metode x Otsu untuk mendapatkan citra biner yang kemudian citra ini dilakukan operasi opening dan closing untuk menghilangkan noise pada citra. Setelah itu dilakukan pencarian lokasi dimana kemungkinan terdapat objek lampu depan dengan menggunakan teknik Connected Component. Hasil dari layer pertama kemudian dilakukan klasifikasi dengan Haar feature based AdaBoost. Klasifikasi ini digunakan untuk mengurangi kejadian false alarm dan meningkatkan nilai precision. Hasil dari metode ini yaitu untuk nilai recall mencapai 91.5% dan untuk nilai precision yaitu 93.8%. Hal ini menandakan jumlah mobil yang tidak terdeteksi dan jumlah kesalahan deteksi pada penggunaan metode ini hanya sedikit. ============================================================================================================== Car detection has important role in dynamic traffic management system. It is closely related to the handling of traffic congestion in big cities. In order to optmize the handling of the congestion, the information obtained from this vehicle detection system will be very influential. And this has been the challenge in car detection on highways or freeways. Many methods can be used to detect car object, starting from the use of sensor and also through CCTV camera. The goverment is likely to implement CCTV used to monitor cars on highways or freeways as multifunctional and cheaper tool. Therefore, a car detection system using CCTV image that is accurate and has rapid process is needed. In this final project, there are two layers for car detection in the image. The first layer will be conducted using morphological operations to find a possible location of the headlights. In this layer the Top Hat Transfrom operation is conducted to obtain image contrast, and Otsu method to obtain binary image, then this binary image applied Opening and Closing operation to remove the noise. The connected component technique is then applied to the result to find areas where the possible pairs of headlight locate in image. After that, the result from the first layer is conducted using Haar feature based Adaboost. This classification is done to reduce the incidence of false alarm and increase the precision value. The xii result show that the method can obtain recall value up to 91.5% and the for the precision value up to 93.8%. This result indicate that the false alarm and missing rate only slightly

A two-layer night-time vehicle detector

We present a two-layer night time vehicle detector. At the first layer, headlight detection [ref] is conducted to allocate areas (eg, bounding box) where are the possible pairs of the headlights in the image, the Haar feature based Adaboost framework are then applied to decide the vehicle front at night time. This approach has achieved significant performance for vehicle detection at night time. Our results showed that the proposed algorithm can reach over 90% of detection rate at 1.5% false positive rate. Without any code optimization, it also performs at a faster speed compared to Haar feature based Adaboost approachWeihong Wang, Chunhua Shen, Jian Zhang and Sakrapee Paisitkriangkra