Action recognition with trajectory-pooled deep-convolutional descriptors

TLDR

Visual features are vital for human action understanding in videos. This paper introduces the trajectory‑pooled deep‑convolutional descriptor (TDD), a video representation that combines hand‑crafted and deep‑learned feature advantages. TDDs are built by learning convolutional feature maps with deep networks, pooling them along trajectories, applying spatiotemporal and channel normalizations, and evaluating the resulting descriptors on HMDB51 and UCF101. TDDs outperform prior hand‑crafted and deep‑learned features, achieving state‑of‑the‑art accuracy of 65.9 % on HMDB51 and 91.5 % on UCF101.

Abstract

Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features and deep-learned features. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMDB51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features and deep-learned features. Our method also achieves superior performance to the state of the art on these datasets (HMDB51 65.9%, UCF101 91.5%).