The work focuses on developing an algorithm to assist drivers in lane changing using Light Detection and Ranging (LiDAR) and Computer Vision (CV). It is expected to reduce the number of accidents caused by improper lane switching. It proceeds with the help of the dataset taken from Karlsruhe Institute of Technology and Toyota Technological Institute of Chicago (KITTI) vision benchmark suite which consists of both LiDAR and CV samples. By combining the results obtained from CV with the LiDAR data, it is possible to predict the distance between the car and the nearest obstacle. An algorithm is developed to indicate the availability of a free lane with the data obtained.
Emotion Recognition (ER) systems is very much important for interpersonal relationship. Emotions are developed by some physiological changes. The straightforward of this effort is to discover the competence of language and facemask elements to deliver the feeling exact information for enhancing the Human-Machine interaction. The techniques and systems used in emotion detection may vary depending on the features inspected. Since both these features complement each other, combining them results in higher performance in terms of accuracy of 94.734%. The proposed system was tested on ENTERFACE’05 database and real time video. For Video, Speeded Up Robust Features (SURF) and Gabor features are used.
The agricultural domain in past few decades has seen a decrease in its productivity. The main cause for this was found to be an increase in plant diseases. Having diseases in plants is quite common, but due to improper care there have been serious effects on plants. But we cannot keep inspecting each and every plant present in thousands. Hence, in this work an approach is developed which provides faster and more accurate results of the detected plant leaves and its corresponding diseases. The proposed work approach uses various image processing techniques for recognising the plant leaf type and detecting disease. The system uses two different classification methods namely, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) and their performances are compared.
