TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

TL;DR

TraMNet introduces a transition matrix approach for efficient and accurate spatiotemporal action localization by modeling actor and camera movements, reducing computational complexity and improving detection robustness.

Contribution

The paper proposes a novel transition-matrix-based network that models movement between anchor proposals, enabling efficient and translation-invariant action tube proposals with sparse annotations.

Findings

Reduces proposal search space from exponential to manageable size.

Achieves effective action localization on multiple datasets.

Handles sparse annotations effectively.

Abstract

Current state-of-the-art methods solve spatiotemporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called \textit{action micro-tubes}. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera does. Assuming we evaluate $n$ 2D anchors in each frame, then the number of possible transitions from each 2D anchor to the next, for a sequence of $f$ consecutive frames, is in the order of $O(n^f)$, expensive even for small values of $f$. To avoid this problem, we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from $O(n^f)$ to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translation-invariant. Finally, we show that our network can handle sparse annotations such as those available in the DALY dataset. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.