With the recent developments in Artificial Intelligence, vehicle detection systems have become an essential part of many sectors like transport, automobile, security, law enforcement, and traffic management. This increased the requirement for an efficient system for vehicle detection. The main focus of our work is to find the best algorithm which can be used to design a vehicle detection system. For this, we compare two well-known deep learning algorithms which are Faster R-CNN and Single Shot Detector (SSD) algorithms. Both of the Pre-trained models of Tensorflow were tested on a dataset of hundred images with cars in them. It was found that Faster R-CNN is better with an accuracy score of 82.75 but was slower than SSD, whereas SSD had an accuracy score of 80.58 but was faster compared to Faster R-CNN.

To interact with world using expressions or body movements is comparatively effective than just speaking. Gesture recognition can be a better way to convey meaningful information. Communication through gestures has been widely used by humans to express their thoughts and feelings. Gestures can be performed with any body part like head, face, hands and arms but most predominantly hand is use to perform gestures, Hand Gesture Recognition have been widely accepted for numerous applications such as human computer interactions, robotics, sign language recognition, etc. This paper focuses on bare hand gesture recognition system by proposing a scheme using a database-driven hand gesture recognition based upon skin color model approach and thresholding approach along with an effective template matching with can be effectively used for human robotics applications and similar other applications .Initially, hand region is segmented by applying skin color model in YCbCr color space. Y represents the luminance and Cb and Cr represents chrominance. In the next stage Otsu thresholding is applied to separate foreground and background. Finally, template based matching technique is developed using Principal Component Analysis (PCA), k-nearest neighbour (KNN) and Support Vector Machine (SVM) for recognition. KNN is used for statistical estimation and pattern recognition. SVM can be used for classification or regression problems.