Automatic License Plate Recognition is a crucial task for several applications related to Intelligent Transportation Systems, from access control to traffic monitoring. Most existing approaches are focused on a specific setup (e.g., toll control) or a single license plate (LP) region (e.g., European, US, Brazilian, Taiwanese, etc.), which limits their application. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. We present an Improved Warped Planar Object Detection Network (IWPOD-NET) that is able to detect the four corners of an LP in a variety of conditions, so that it can be warped to a fronto-parallel view and alleviate perspective-related distortions. Given the rectified LP, we test two different Optical Character Recognition (OCR) methods based on object detection. Our experimental results show that the proposed detector is competitive with state-of-the-art (SOTA) methods using a very limited training set. Regarding the full ALPR results, our method achieves top-scoring results for several datasets that include a variety of capture conditions and vehicle types (in particular, motorcycles).

This paper presents a new audiovisual voice activity detection (VAD) method for off-the-shelf cameras presenting a color sensor and two microphones. The motion of particles in the mouth region of each face detected by the camera is used as video cue, while the Generalized Cross Correlation with the PHase Transform (GCC-PHAT) is used as audio cue. We then estimate the distribution of the audiovisual cues and perform the final VAD result for each detected face using a Hidden Markov Model (HMM). Experimental results indicated that our method achieves an average 87% accuracy for a set of test videos.