Oct 262018
 

Mateen‘s paper on “Implementation and Evaluation of IoT System Using Cloud Storage Platform” has been accepted in the 2nd International Conference on Big Data and Internet of Things (BDIOT 2018).

Abstract. As the internet has become more accessible, the use of Internet of Things (IoT) systems is increasing. An IoT system can be accessed either by directly connecting to the network configured with external access and appropriate port forwarding; or through a cloud server. Direct access through network may not be possible for networks with restriction on external access, and requires technical know-how to configure the port forward. The use of cloud server is convenient that the users simply have to sign up an account at and use user friendly application to setup the system. This approach however requires the manufacturer to main the
cloud server, has the concern of having users’ data accessible to the owner of the cloud server, and does not allow interoperability of devices and systems across different manufacturers. In this paper, we have proposed the use of publicly available cloud storage services to provide the accessibility of the IoT devices
without relying on a specific cloud server, and without requiring the manufacturer to maintain their own cloud server. The proposed approach will allow interoperability between devices or systems from different manufacturers. This paper describes the concept, implementation and evaluation of the proposed cloud
storage based IoT system. The proposed IoT system has been implemented to work with Google Drive, Dropbox and Microsoft OneDrive. The performance of the proposed system has been evaluated against dedicated IoT cloud servers including Microsoft Azure IoT, Google IoT, CloudMQTT (CMQTT) and Eclipse IoT. The results show that can be effectively used in systems that are not time critical.

Details of the works for this paper are available here.

Jul 212018
 

Bacha‘s paper on “Using Margin-based Region of Interest Technique with Multi-Task Convolutional Neural Network and Template Matching for Robust Face Detection and Tracking System ” has been accepted in the 2nd International Conference on Imaging, Signal Processing and Communication (ICISPC 2018).

Abstract. Real-time face detection and tracking systems suffer from low accuracy and slow processing speed that lead to poor robustness. This problem is vital in real-time setups including human robot interactions (HRI) and video analysis systems. This paper presents margin-based region of interest (MROI) approach to speed up the processing time. Further a hybrid approach is also presented that combines Multi-task Convolutional Neural Networks (MTCNN) with template matching to improve face detection accuracy. The MROI approach which is responsible to speed up the processing time is presented in two variants with fixed and dynamic margin
concepts. Dataset used in this work comprises of twenty RGB video files. Each video file is fifteen seconds long and been created from real-life videos containing a person in lecture delivering environment. Each video file contains a person in which the person moves, turns head and the camera also moves. The highest face detection and tracking accuracy achieved in this paper is 99.31% with a processing time of 14.93 milliseconds per frame. The proposed hybrid algorithm significantly improves the ability to detect and track faces in sideway orientation apart from frontal face. The proposed algorithm has the ability to process above 65 frames per second (FPS). The system presented has increased FPS processing ability by more than 400% as well as given boost to the accuracy if compared to the conventional MTCNN full frame scanning techniques.

Details of the works for this paper are available here.

Nov 212017
 

Bacha‘s paper on “Hybrid Model with Margin-Based Real-Time Face Detection and Tracking” has been accepted in the 11th Multi-disciplinary International Workshop on Artificial Intelligence (MIWAI 2017).

Abstract. Face detection and tracking algorithms mainly suffer from low accuracy, slow processing speed, and poor robustness when meet with real-time setup. The problem becomes crucial in real-time situations such as in human robot interactions (HRI) or video analysis. A margin-based region of interest (ROI) hybrid approach that combines Haar cascade and template matching for face detection and tracking is proposed in this paper to improve the detection accuracy and processing speed. To speed up the processing time, region of interests (ROIs) with fixed and dynamic margin concepts are used. A dataset comprising of ten RGB video streams of fifteen seconds have been created from real-life videos containing a person in lecture delivering environment. In each video, there exists person’s movement, face turning and camera movements. An accuracy of 97.96% with processing time of 10.76 milliseconds per frame has been achieved. The proposed algorithm can detect and track faces in sideway orientation apart from frontal face. The proposed approach can process the video streams at the speed above 90 frames per second (FPS). The proposed approach reduces processing time by ten times and with a boost to accuracy in comparison to the conventional full frame scanning techniques.

Details of the works for this paper are available here.