Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier

Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job.  Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices

Real-time vehicle detection using low-cost sensors

Improving road safety and reducing the number of accidents is one of the top priorities for the automotive industry. As human driving behaviour is one of the top causation factors of road accidents, research is working towards removing control from the human driver by automating functions and finally introducing a fully Autonomous Vehicle (AV). A Collision Avoidance System (CAS) is one of the key safety systems for an AV, as it ensures all potential threats ahead of the vehicle are identified and appropriate action is taken. This research focuses on the task of vehicle detection, which is the base of a CAS, and attempts to produce an effective vehicle detector based on the data coming from a low-cost monocular camera. Developing a robust CAS based on low-cost sensor is crucial to bringing the cost of safety systems down and in this way, increase their adoption rate by end users. In this work, detectors are developed based on the two main approaches to vehicle detection using a monocular camera. The first is the traditional image processing approach where visual cues are utilised to generate potential vehicle locations and at a second stage, verify the existence of vehicles in an image. The second approach is based on a Convolutional Neural Network, a computationally expensive method that unifies the detection process in a single pipeline. The goal is to determine which method is more appropriate for real-time applications. Following the first approach, a vehicle detector based on the combination of HOG features and SVM classification is developed. The detector attempts to optimise performance by modifying the detection pipeline and improve run-time performance. For the CNN-based approach, six different network models are developed and trained end to end using collected data, each with a different network structure and parameters, in an attempt to determine which combination produces the best results. The evaluation of the different vehicle detectors produced some interesting findings; the first approach did not manage to produce a working detector, while the CNN-based approach produced a high performing vehicle detector with an 85.87% average precision and a very low miss rate. The detector managed to perform well under different operational environments (motorway, urban and rural roads) and the results were validated using an external dataset. Additional testing of the vehicle detector indicated it is suitable as a base for safety applications such as CAS, with a run time performance of 12FPS and potential for further improvements.</div

Robust object detection in the wild via cascaded DCGAN

This research deals with the challenges of object detection at a distance or low resolution in the wild. The main intention of this research is to exploit and cascade state-of-the-art models and propose a new framework for enabling successful deployment for diverse applications. Specifically, the proposed deep learning framework uses state-of-the-art deep networks, such as Deep Convolutional Generative Adversarial Network (DCGAN) and Single Shot Detector (SSD). It combines the above two deep learning models to generate a new framework, namely DCGAN-SSD. The proposed model can deal with object detection and recognition in the wild with various image resolutions and scaling differences. To deal with multiple object detection tasks, the training of this network model in this research has been conducted using different cross-domain datasets for various applications. The efficiency of the proposed model can further be determined by the validation of diverse applications such as visual surveillance in the wild in intelligent cities, underwater object detection for crewless underwater vehicles, and on-street in-vehicle object detection for driverless vehicle technologies. The results produced by DCGAN-SSD indicate that the proposed method in this research, along with Particle Swarm Optimization (PSO), outperforms every other application concerning object detection and demonstrates its great superiority in improving object detection performance in diverse testing cases. The DCGAN-SSD model is equipped with PSO, which helps select the hyperparameter for the object detector. Most object detectors struggle in this regard, as they require manual effort in selecting the hyperparameters to obtain better object detection. This research encountered the problem of hyperparameter selection through the integration of PSO with SSD. The main reason the research conducted with deep learning models was the traditional machine learning models lag in accuracy and performance. The advantage of this research and it is achieved with the integration of DCGAN-SSD has been accommodated under a single pipeline