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Person Retrieval from unconstrained surveillance videos
Person Retrieval from unconstrained surveillance videos
About :
The IEEE Computational Intelligence Society Chapter - IEEE Gujarat Section brings in a Symposium series to foster exchange of ideas and experience by the young researchers. It provides a discussion forum for advancement of their research and gain feedback on research work in the domain of computational intelligence. This is an initiative by IEEE CIS chapter - IEEE Gujarat section to build community for intellectual exchange and support the budding researchers.
Intended Participants: Anyone interested in Computational Intelligence Domain
Abstract:
Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using a textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a tall man with a white t-shirt and blue jeans carrying a backpack. He has brown hair. Such a description contains attributes like gender, height, type of clothing, colour of clothing, hair colour, and accessories. Such attributes are formally known as soft biometrics. They help bridge the semantic gap between a human description and a machine as a textual query contains the person's soft biometric attributes. Traditional biometric (e.g., face) fails to locate a person in the surveillance video due to low resolution, distance, and unconstrained environment. In such cases, soft biometric attributes like gender, clothing colour, and type are still deducible. It is also not feasible to manually search through huge volumes of surveillance footage to retrieve a specific person. Hence, automatic person retrieval using vision and language-based algorithms is becoming popular.
Date: 26.07.2021
Time: 11.00 am to 11.50 am: 35 Mins talk + 15 Mins. Q&A
Speaker: Mr. Hiren Galiyawala, PhD Scholar, Ahmedabad University, India
Supervisor: Dr Mehul S Raval, Professor, Ahmedabad University, India
About Speaker: Hiren J Galiyawala is a PhD scholar at Ahmedabad University and working as a Data Scientist at Rydot Infotech Pvt. Ltd. He obtained a Bachelor's degree (ECE) in 2007 from VNSGU and a Master's degree (Digital Systems – Electronics) in 2010 from the College of Engineering Pune / University of Pune, India. He has 11+ years of experience including industry, research, and academic. His research interest is in computer vision, biometrics, and video analytics. His publications are in reputed conferences and journals with leading publishers – IEEE, Elsevier, and Springer. He also holds professional certifications like International Software Testing Qualifications Board (ISTQB) and IBM Certified Solution Designer – Rational Functional Tester for JAVA.
Registration: Interested may please register at https://forms.gle/6jHs8cy53cvrpu8Q8 and we will mail you the meeting details.
Expert Talk: Cellular Planning and Optimization for the Next Generation Wireless Network
Expert Talk: Cellular Planning and Optimization for the Next Generation Wireless Network
Computational Intelligence Society (CIS), Gujrat Chapter is glad to announce next talk under Research symposium series 2021 as details given below:
Title : Cellular Planning and Optimization for the Next Generation Wireless Network
Speaker : Dr Sarosh Dastoor – Asst. Professor , SCET
Supervisors : Dr. Upena Dalal and Dr. Jignesh sarvaiya (SVNIT , Surat)
Date : 23 April 2021 (Friday)
Timings : 1.30 to 2:30 PM (IST)
Virtual Research Symposium Series by IEEE CIS Chapter – IEEE Gujarat Section-2
Virtual Research Symposium Series by IEEE CIS Chapter – IEEE Gujarat Section-2
About:
The IEEE Computational Intelligence Society Chapter – IEEE Gujarat Section brings in Symposium series to foster exchange of ideas and experience. It provides a discussion forum for advancement of their research and gain feedback on research work in the domain of computational intelligence. This is an initiative by IEEE CIS chapter – IEEE Gujarat section to build community for intellectual exchange and support the budding researchers.
Intended Participants: Anyone interested in Computational Intelligence Domain
Details of the next Symposium talk
Topic: Recent trends in Brain Tumor Analysis
Date: 23.03.2021
Time: 11.00 am to 11.45 am
Speaker: Ms. Rupal Agravat, PhD Scholar, Ahmedabad University, India
Supervisor: Dr Mehul S Raval, Professor, Ahmedabad University, India
Cosupervisor: Dr Sanjay Chaudhary, Professor, Ahmedabad University, India
About Speaker: Rupal Agravat is faculty at the Institute of Technology, Nirma University. She is pursuing her PhD on “Robust Brain Tumor Segmentation for Overall Survival Prediction”. She has 18 years of teaching experience. Her research area includes Digital Image Processing, Medical Imaging, and Deep Learning. She has published around 15 research articles in National / International Journal/Conferences.
Virtual Research Symposium Series by IEEE CIS Chapter – IEEE Gujarat Section
Virtual Research Symposium Series by IEEE CIS Chapter – IEEE Gujarat Section
Date: 17 Feb 2021
Time: 11:00 AM to 11:45 AM
About:
The IEEE Computational Intelligence Society Chapter – IEEE Gujarat Section brings in Symposium series to foster exchange of ideas and experience. It provides a discussion forum for advancement of their research and gain feedback on research work in the domain of computational intelligence. This is an initiative by IEEE CIS chapter – IEEE Gujarat section to build community for intellectual exchange and support the budding researchers.
Intended Participants: Anyone interested in Computational Intelligence Domain
Details of the next Symposium talk
Speaker: Mr Jayesh Munjani, PhD Scholar, Uka Tarsadia University, Bardoli, India
Supervisor: Dr Maulin Joshi, Professor & Head, Electronics and Communication Engineering department, Sarvajanik College of Engineering &Technology, Surat
Topic: A non-conventional lightweight Auto Regressive Neural Network for accurate and energy efficient target tracking in Wireless Sensor Network
Abstract:
The design of an energy-efficient tracking framework is a well-investigated issue and a prominent sensor network application. The current research state shows a clear scope for developing algorithms that can work, accompanying both energy efficiency and accuracy. The prediction-based algorithms can save network energy by carefully selecting suitable nodes for continuous target tracking. However, the conventional prediction algorithms are confined to fixed motion models and generally fail in accelerated target movements. The neural networks can learn any non-linearity between input and output as they are model-free estimators. To design a lightweight neural network-based prediction algorithm for resource-constrained tiny sensor nodes is a challenging task. This research aims to develop a simpler, energy-efficient, and accurate network-based tracking scheme for linear and non-linear target movements. The proposed technique uses an autoregressive model to learn the temporal correlation between successive samples of a target trajectory. The simulation results are compared with the traditional Kalman filter (KF), Interacting Multiple models (IMM), Current Statistical model (CSM), Long Short Term Memory (LSTM), Decision Tree (DT), and Random Forest (RF) based tracking approach. It shows that the proposed algorithm can save up to 70% of network energy with improved prediction accuracy.
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