This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentation, we decided to explore an object detection task using a particular model architecture which maintains a sweet spot between computational cost and accuracy. The model architecture which we had used is LSTM-Decoder. The model had been experimented with different CNN feature extractor and found their pros and cons in variant scenarios. The results which we had obtained on different datasets elucidates that CNN plays a major role in obtaining higher accuracy and we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection Dataset. In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as follows: using the detected annotation from object detection, a small cropped image is generated which would be feed into a small cascade network which was trained for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently, we had generated our results for shoulder keypoint detection. However, this idea could be extended to full-body pose Estimation by modifying the cascaded network for pose estimation purpose and this had become an important topic of discussion for the future work of this thesis.

Creating a fitness function for music is largely subjective and dependent on a programmer's personal tastes or goals. Previous attempts to create musical fitness functions for use in genetic algorithms lack scope or are prejudiced to a certain genre of music. They also suffer the limitation of producing music only in the strict style determined by the programmer. We show in this paper that musical feature extractors that avoid the challenges of qualitative judgment enable creation of a multi-objective function for direct music production. Multi-objective fitness functions enable creation of music with varying identifiable styles. With this system we produced three distinct groups of music which computationally cluster into distinct styles as described by the set of feature extractors. We also show that knowledgeable individuals make similar clusters while a random sample of people make some similar and some different clusterings.