Vehicle identification using fuzzy adaline neural network

Abstract

Video surveillance is an important aspect in today's world, where a particular scene of area requires monitoring to avoid terrorist attacks and unauthorized entries. Vehicle recognition is an important area in object tracking and recognition. Objects may be of rigid or non-rigid in nature with varying velocity and have different features. Important features like shape, logo, color and texture are complex in nature. Hence there is a need of better algorithm for detecting and identifying the objects like car. A new method is proposed for recognizing the cars present in the video. At first the features like shape is extracted using moments, logo using the Scale Invariant Feature Transform (SIFT) and the RGB color values of the car body. Using these features the recognition is carried out to classify the type of car. Recognition of cars has range of application like, military surveillance, traffic management, autonomous navigation system, auto parking.

Vacant Parking Lot Information System Using Transfer Learning and IoT

“…Vision based systems with CCTV surveillance are scalable and cost-effective for large outdoor parking spaces. They have been used for multiple applications, such as occupancy detection, vehicle type detection [4,5] and theft prevention [6]. The existing methods for occupancy detection (finding the presence or absence of cars at parking lots) employ a two-part strategy: extracting relevant features from camera images (obtained from the parking field) and training a classifier.…”