Browsing by Author "Ramanan, A"

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    item: Conference-Abstract
    Human action detection using space-time interest points
    (Engineering Research Unit, Faculty of Engiennring, University of Moratuwa, 2016-04) Sriashalya, S; Ramanan, A; Jayasekara, AGBP; Amarasinghe, YWR
    The bag-of-features (BoF) approach for human action classification uses spatio-temporal features to assign the visual words of a codebook. Space time interest points (STIP) feature detector captures the temporal extent of the features, allowing distinguishing between fast and slow movements. This study compares the relative performance of action classification on KTH videos using the combination of STIP feature detector with histogram of gradient orientations (HOG) and histograms of optical flow (HOF) descriptors. The extracted descriptors are clustered using K-means algorithm and the feature sets are classified with two classifiers: nearest neighbour (NN) and support vector machine (SVM). In addition, this study compares actionspecific and global codebook in the BoF framework. Furthermore, less discriminative visual words are removed from initially constructed codebook to yield a compact form using likelihood ratio measure. Testing results show that STIP with HOF performs better than HOG descriptors and simple linear SVM outperforms NN classifier. It can be noticed that action-specific codebooks when merged together perform better than globally constructed codebook in action classification on videos.
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    item: Article-Full-text
    Transformers in single object tracking
    (IEEE, 2023) Kugarajeevan, J; Kokul, T; Ramanan, A; Fernando, S
    Single-object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single-object tracking by introducing new perspectives and achieving superior tracking robustness. In this paper, we conduct an in-depth literature analysis of Transformer tracking approaches by categorizing them into CNN-Transformer based trackers, Two-stream Two-stage fully-Transformer based trackers, and One-stream One-stage fully-Transformer based trackers. In addition, we conduct experimental evaluations to assess their tracking robustness and computational efficiency using publicly available benchmark datasets. Furthermore, we measure their performances on different tracking scenarios to identify their strengths and weaknesses in particular situations. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they encounter, and the future directions they may take.