BSN: Boundary Sensitive Network for Temporal Action Proposal Generation (1806.02964v3)

Abstract: Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content. This problem requires methods not only generating proposals with precise temporal boundaries, but also retrieving proposals to cover truth action instances with high recall and high overlap using relatively fewer proposals. To address these difficulties, we introduce an effective proposal generation method, named Boundary-Sensitive Network (BSN), which adopts "local to global" fashion. Locally, BSN first locates temporal boundaries with high probabilities, then directly combines these boundaries as proposals. Globally, with Boundary-Sensitive Proposal feature, BSN retrieves proposals by evaluating the confidence of whether a proposal contains an action within its region. We conduct experiments on two challenging datasets: ActivityNet-1.3 and THUMOS14, where BSN outperforms other state-of-the-art temporal action proposal generation methods with high recall and high temporal precision. Finally, further experiments demonstrate that by combining existing action classifiers, our method significantly improves the state-of-the-art temporal action detection performance.

• The paper introduces BSN’s novel framework for generating temporal action proposals by precisely estimating action boundaries.
• It employs a local-to-global strategy with temporal evaluation, proposal pairing, and boundary-sensitive features to capture diverse action durations.
• Experimental results on ActivityNet-1.3 and THUMOS14 show improved AUC and AR metrics, demonstrating BSN's effectiveness for video analysis.

Boundary Sensitive Network for Temporal Action Proposal Generation

The paper presents the Boundary Sensitive Network (BSN), a novel method for temporal action proposal generation in video analysis. This research addresses the challenge of generating temporal proposals that need precise boundaries and high recall from video content, particularly in untrimmed videos with extensive irrelevant sections.

The BSN architecture employs a "local to global" strategy, comprising three key components: temporal evaluation, proposal generation, and proposal evaluation modules. This approach allows for the creation of flexible proposals with accurate temporal demarcations.

Architectural Components

1. Temporal Evaluation Module: This module implements temporal convolutional neural networks to assess the likelihood of each temporal location being a start, end, or within an action. It produces three probability sequences for starting, ending, and actionness events.
2. Proposal Generation Module: Proposals are formed by pairing high probability start and end points, thus allowing for diverse durations and precise temporal borders. The module further constructs a Boundary-Sensitive Proposal (BSP) feature which encapsulates local semantic information essential for the next phase.
3. Proposal Evaluation Module: This component evaluates proposals using BSP features to compute a confidence score signifying the presence of action within a proposal. These confidence scores are utilized to retrieve the most relevant proposals effectively.

Experimental Validation

The BSN was tested on two datasets, ActivityNet-1.3 and THUMOS14, showing superior performance against existing methods. On ActivityNet-1.3, BSN attained an Area Under Curve (AUC) of 66.17% on the validation set, outperforming other approaches. On THUMOS14, it achieved an AR@50 of 37.46%, indicating its effectiveness across varying thresholds.

Strong Claims

The paper argues that BSN's reliance on boundary probabilities enables precise action localization with flexible proposal parameters, surpassing fixed-duration sliding window techniques. Furthermore, the BSP feature facilitates significant improvements in retrieval accuracy, making the confidence scores robust indicators in proposal selection.

Implications

Practically, BSN's methodology benefits applications like video surveillance and content recommendation systems, where precise action detection in cluttered data is vital. Theoretically, it underscores the application of localized boundary probabilistic assessments in proposal generation, paving the way for advanced models in temporal understanding.

Future Directions

The current research opens several avenues for exploration. One potential direction is enhancing the boundary probability module with advanced temporal models like transformers. Another is optimizing the scale and granularity of the BSP feature for even finer action detection. Furthermore, increasing the generalization of BSN to diverse video domains without retraining is a promising goal.

In conclusion, the BSN framework significantly enhances temporal action proposal generation by aligning local and global information, thereby setting a high benchmark for future developments in temporal video analysis.