Seyed shahabeddin Nabavi

Ph.D. Student, Lassonde School of Engineering , York University
nabaviss@eecs.yorku.ca

I am a first year Ph.D. student at York University under the supervision of Prof. James Elder. I received my M.Sc in Computer Science from the University of Manitoba where I was advised by Dr. Yang Wang. My current research focus is on unsupervised single view 3D reconstruction.

Research Interests

Deep Learning, Computer Vision, Machine Learning, Unsupervised Learning, 3D Scene Understanding, Simultaneous Localization and Mapping

Publications

Future Semantic Segmentation with Convolutional LSTM

Seyed shahabeddin Nabavi, Mrigank Rochan, Yang Wang

We consider the problem of predicting semantic segmentation of future frames in a video. Given several observed frames in a video, our goal is to predict the semantic segmentation map of future frames that are not yet observed. A reliable solution to this problem is useful in many applications that require real-time decision making, such as autonomous driving. We propose a novel model that uses convolutional LSTM (ConvLSTM) to encode the spatiotemporal information of observed frames for future prediction. We also extend our model to use bidirectional ConvLSTM to capture temporal information in both directions. Our proposed approach outperforms other state-of-the-art approaches on the benchmark dataset.
[bibTex][pdf][arxiv][poster][code]

British Machine Vision Conference
(BMVC), 2018

Future Frame Prediction Using Convolutional VRNN for Anomaly Detection

Yiwei Lu,Mahesh Kumar K , Seyed shahabeddin Nabavi,Yang Wang

Anomaly detection in videos aims at reporting anything that does not conform the normal behaviour or distribution. However, due to the sparsity of abnormal video clips in real life, collecting annotated data for supervised learning is exceptionally cumbersome. Inspired by the practicability of generative models for semi-supervised learning, we propose a novel sequential generative model based on variational autoencoder (VAE) for future frame prediction with convolutional LSTM (ConvLSTM). To the best of our knowledge, this is the first work that considers temporal information in future frame prediction based anomaly detection framework from the model perspective. Our experiments demonstrate that our approach is superior to the state-of-the-art methods on three benchmark datasets.
[bibTex][pdf][arxiv][poster][code]

IEEE International Conference on Advanced Video and Signal-based Surveillance
(AVSS), 2019