Classifiers The haze free images are fed to a convolutional network for feature engineering and the output is fed to a fully connected network for classification. Multiple color spaces (RGB, HSV and CIE LAB) are tested to determine the color space which is easiest to interpret and leads to higher performance. The final model, when given an image of a vehicle, will be able to predict the color of the vehicle with high accuracy.

Dataset Used The Vehicle Color Recognition Dataset contains vehicle images in eight colors, which are black, blue, cyan, gray, green, red, white and yellow. The images are taken in the frontal view captured by a high-definition camera with the resolution of × on the urban road. The collected data set is very challenging due to the noise caused by illumination variation, haze, and over exposure. The dataset is available at -

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Dehazing of images A major part of this project will deal with the removal of haze from images. This algorithm can be applied to many other problems too. This algorithm uses atmospheric light to give us clearer images. As cities grow bigger, we are encountering more air and light pollution. Both contribute to hazy images that are difficult to process in real time. We will be using transmission maps and atmospheric light to generate a clear image.

Problem Statement In recent years the number of cars on roads has increased exponentially and, identifying them has become a very big task. Vehicle color information is one of the important elements in ITS (Intelligent Traffic System), the other two being– Make and model of the car and license plate recognition. Vehicle color is an important property for vehicle identification and provides visual cues for fast action law enforcement. Recognizing the color of a moving or even a still vehicle can be a very challenging task because of several factors including weather conditions, quality of video/image acquisition, and strip combination of the vehicle. Different ensemble algorithms can be used to give us color recognition.