Browsing by Author "Ramanan, A."

Browsing by Author "Ramanan, A."

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  • Deep Match Tracker Classifying when Dissimilar, Similarity Matching when Not 
    Kokul, T.; Fookes, C.; Sridharan, S.; Ramanan, A.; Pinidiyaarachchi, U.A.J. (IEEE, IEEE International Conference on Image Processing (ICIP)., 07-10-18)
    Visual tracking frameworks employing ConvolutionalNeural Networks (CNNs) have shown state-of-the-art performancedue to their hierarchical feature representation. Whileclassification and update based deep neural net tracking ...
  • An Efficient Bag-of-Feature Representation for Object Classification 
    Vinoharan, Veerapathirapillai; Ramanan, A. (ELCVIA, 16-12-21)
    The Bag-of-features (BoF) approach has proved to yield better performance in a patch- based object classification system owing to its simplicity. However, often the very large number of patch-based descriptors (such as ...
  • Gate connected convolutional neural network for object tracking 
    Kokul, T.; Fookes, C.; Sridharan, C.; Ramanan, A.; Pinidiyaarachchi, U.A.J. (IEEE, IEEE International Conference on Image Processing (ICIP), 17-09-17)
    Convolutional neural networks (CNNs) have been employedin visual tracking due to their rich levels of feature representation.While the learning capability of a CNN increaseswith its depth, unfortunately spatial information ...
  • Online Multi-Person Tracking-by-detection Method Using ACF and Particle Filter 
    Kokul, T.; Ramanan, A.; Pinidiyaarachchi, U.A.J. (IEEE, IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS), 12-12-15)
    Automatically detecting and tracking multiple persons in videos is one of the main research interests in computer vision based applications. This paper presents a tracking-by-detection approach for tracking people in dynamic ...
  • Target-Specific Siamese Attention Network for Real-time Object Tracking 
    Kokul, T.; Fookes, C.; Sridharan, S.; Ramanan, A.; Pinidiyaarachchi, A. (IEEE Transactions on Information Forensics and Security, 16-08-19)
    Deep similarity trackers are able to track above real-time speed. However, their accuracy is considerably lower than deep classification based trackers since they avoid valuable online cues. To feed the target-specific ...