This paper represents a cost-effective scene perception system aimed towards visually impaired individual. We use an odroid system integrated with an USB camera and USB laser that can be attached on the chest. The system classifies the detected objects along with its distance from the user and provides a voice output. Experimental results provided in this paper use outdoor traffic scenes.The object detection and classification framework exploits a multi-modal fusion based faster RCNN using motion, sharpening and blurring filters for efficient feature representation.

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This research mainly emphasizes on traffic detection thus essentially involving object detection and classification. The particular work discussed here is motivated from unsatisfactory attempts of re-using well known pre-trained object detection networks for domain specific data. In this course, some trivial issues leading to prominent performance drop are identified and ways to resolve them are discussed. For example, some simple yet relevant tricks regarding data collection and sampling prove to be very beneficial. Also, introducing a blur net to deal with blurred real time data is another important factor promoting performance elevation. We further study the neural network design issues for beneficial object classification and involve shared, region-independent convolutional features. Adaptive learning rates to deal with saddle points are also investigated and an average covariance matrix based pre-conditioned approach is proposed.We also introduce the use of optical flow features to accommodate orientation information. Experimental results demonstrate that this results in a steady rise in the performance rate.

Edge detection is an important pre-processing step for any image processing application, object recognition and emotion detection. Edge detection is very helpful in case of noise free images. But in case of noisy images it is a challenging task.Noisy images are corrupted images. Their parameters are difficult to analyze and detect. In this research work different filters are used for the filtration of the image and to analyze that what exact difference it makes when it comes to detect t he edge of the image. It includes the comparative study of various image denoising filters. These Filters are then applied withBFO Algorithm and they are compared with one another which help to calculate the parameters of noisy images. The comparison parameters which have been taken into contrast are Peak Signal to Noise Ratio, Mean Square Error and Noise Suppression Rate.

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