2 research outputs found
End-to-end Flow Correlation Tracking with Spatial-temporal Attention
Discriminative correlation filters (DCF) with deep convolutional features
have achieved favorable performance in recent tracking benchmarks. However,
most of existing DCF trackers only consider appearance features of current
frame, and hardly benefit from motion and inter-frame information. The lack of
temporal information degrades the tracking performance during challenges such
as partial occlusion and deformation. In this work, we focus on making use of
the rich flow information in consecutive frames to improve the feature
representation and the tracking accuracy. Firstly, individual components,
including optical flow estimation, feature extraction, aggregation and
correlation filter tracking are formulated as special layers in network. To the
best of our knowledge, this is the first work to jointly train flow and
tracking task in a deep learning framework. Then the historical feature maps at
predefined intervals are warped and aggregated with current ones by the guiding
of flow. For adaptive aggregation, we propose a novel spatial-temporal
attention mechanism. Extensive experiments are performed on four challenging
tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed
method achieves superior results on these benchmarks.Comment: Accepted in CVPR 201
Discriminative Image Warping with Attribute Flow
We address the problem of finding deformation between two images for the purpose of recognizing objects. The challenge is that discriminative features are often transformation-variant (e.g. histogram of oriented gradients, texture), while transformation-invariant features (e.g. intensity, color) are often not discriminative. We introduce the concept of attribute flow which explicitly models how image attributes vary with its deformation. We develop a non-parametric method to approximate this using histogram matching, which can be solved efficiently using linear programming. Our method produces dense correspondence between images, and utilizes discriminative, transformation-variant features for simultaneous detection and alignment. Experiments on ETHZ shape categories dataset show that we can accurately recognize highly deformable objects with few training examples. 1