M Pallavi Venugopal
Bio: M Pallavi Venugopal is an academic researcher from Indian Institute of Space Science and Technology. The author has contributed to research in topics: Tracking system & Tracking (particle physics). The author has an hindex of 1, co-authored 1 publications receiving 3 citations.
••02 Mar 2017
TL;DR: The main objective of the paper is to recommend an essential improvement to the existing Multi-Domain Convolutional Neural Network tracker (MDNet) which is used to track unknown object in a video-stream.
Abstract: The main objective of the paper is to recommend an essential improvement to the existing Multi-Domain Convolutional Neural Network tracker (MDNet) which is used to track unknown object in a video-stream. MDNet is able to handle major basic tracking challenges like fast motion, background clutter, out of view, scale variations etc. through offline training and online tracking. We pre-train the Convolutional Neural Network (CNN) offline using many videos with ground truth to obtain a target representation in the network. In online tracking the MDNet uses large number of random sample of windows around the previous target for estimating the target in the current frame which make its tracking computationally complex while testing or obtaining the track. The major contribution of the paper is to give guided samples to the MDNet rather than random samples so that the computation and time required by the CNN while tracking could be greatly reduced. Evaluation of the proposed algorithm is done using the videos from the ALOV300++ dataset and the VOT dataset and the results are compared with the state of art trackers.
TL;DR: In this paper, the YOLOv3 pretraining model is used for ship detection, recognition, and counting in the context of intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making.
Abstract: Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for ...
••03 Jul 2019
TL;DR: A novel face recognition method for population search and criminal pursuit in smart cities and a cloud server architecture for face recognition in smart city environments are proposed.
Abstract: Face recognition technology can be applied to many aspects in smart city, and the combination of face recognition and deep learning can bring new applications to the public security. The use of deep learning machine vision technology and video-based image retrieval technology can quickly and easily solve the current problem of quickly finding the missing children and arresting criminal suspects. The main purpose of this paper is to propose a novel face recognition method for population search and criminal pursuit in smart cities. In large and medium-sized security, the face pictures of the most similar face images can be accurately searched in tens of millions of photos. The storage requires a powerful information processing center for a variety of information storage and processing. To fundamentally support the safe operation of a large system, cloud-based network architecture is considered and a smart city cloud computing data center is built. In addition, this paper proposed a cloud server architecture for face recognition in smart city environments.
01 Jan 2018
TL;DR: Visual tracking is a computer vision problem where the task is to follow a target through a video sequence to solve the problem of tracking blindfolded people in the dark.
Abstract: Visual tracking is a computer vision problem where the task is to follow a targetthrough a video sequence. Tracking has many important real-world applications in several fields such as autonomous v ...