Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval

The Bag-of-Words (BoW) representation is well applied to recent state-of-the-art image retrieval works. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the intersection set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the intersection set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the intersection set. We evaluate our method through extensive experiments on three benchmark datasets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance compared with the state-of-the-art methods.Comment: 8 pages, 7 figures, 6 tables, accepted to CVPR 201

Real-time Automatic License Plate Recognition Using Color Features

Various researchers presented various solutions for license plate detection but real-time performance is still a challenge in the field. In this paper, we propose a fast license plate detection method to work correctly in a real-time environment. In the first step, we locate or detect the license plate in the image sequences. We used color-based methods to detect the license plate. The method consists of computing image contours, later, we analyzed the contours to localize the license plate in the image sequences. After detecting the license plate, in the second step, we perform segmentation using a character recognition model. Finally, we propose the license plate format checking model to verify the detected license plate is the correct license plate. For the tools, we used OpenCV (open computer vision library) and tesseract for character recognition

Minimizing Illumination Effect in License Plate Recognition

The intelligent transportation system is a key technology for efficient traffic control that has been applied in various fields. The existing intelligent transportation system detects the license plates of vehicles mainly through image feature analysis technology by using image-processing techniques. While this method has the advantage of quickly recognizing license plates by simple computing when the environment for recognizing license plate images is favourable, its accuracy is significantly compromised by various environmental changes. This study proposes a method using Faster region-based CNN (R-CNN) and denoising autoencoder technology to improve the recognition performance for tilted and broken plates and false recognition caused by illumination effects in the access control automation system installed at construction sites where these poor conditions frequently occur. This study investigated 3,000 images collected from actual construction sites, comparing the proposed method with the existing Faster R-CNN for license plates affected by various illumination environments, and found an accuracy improvement of more than 30%

An Efficient Method for Number Plate Detection and Extraction Using White Pixel Detection (WPD) Method

Intelligent transport systems play an important role in supporting smart cities because of their promising applications in various areas, such as electronic toll collection, highway surveillance, urban logistics and traffic management. One of the key components of intelligent transport systems is vehicle license plate recognition, which enables the identification of each vehicle by recognizing the characters on its license plate through various image processing and computer vision techniques. Vehicle license plate recognition typically consists of smoothing image using median filter, White pixel detection (WPD), and number plate extraction. In this work an efficient White pixel detection method has been describing a license plates in various luminance conditions. Mostly we will focus on vehicle number plate detection along with the white pixel detection method we will use median filters and Line density filters to increase the detection accuracy for number plate. Subjective and objective quality assessment parameters will give us robustness of proposed work compared to state of License Plate Detection(LPD) techniques

An Intelligent Reconnaissance Framework for Homeland Security

The cross border terrorism and internal terrorist attacks are critical issues for any country to deal with. In India, such types of incidents that breach homeland security are increasing now a day. Tracking and combating such incidents depends only on the radio communications and manual operations of security agencies. These security agencies face various challenges to get the real-time location of the targeted vehicles, their direction of fleeing, etc. This paper proposes a novel application for automatic tracking of suspicious vehicles in real-time. The proposed application tracks the vehicle based on their registration number, type, colour and RFID tag. The proposed approach for vehicle recognition based on image processing achieves 92.45 per cent accuracy. The RFID-based vehicle identification technique achieves 100 per cent accuracy. This paper also proposes an approach for vehicle classification. The average classification accuracy obtained by the proposed approach is 93.3 per cent. An integrated framework for tracking of any vehicle at the request of security agencies is also proposed. Security agencies can track any vehicles in a specific time period by using the user interface of the application