Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos (1608.01529v1)

Abstract: In this work, we propose an approach to the spatiotemporal localisation (detection) and classification of multiple concurrent actions within temporally untrimmed videos. Our framework is composed of three stages. In stage 1, appearance and motion detection networks are employed to localise and score actions from colour images and optical flow. In stage 2, the appearance network detections are boosted by combining them with the motion detection scores, in proportion to their respective spatial overlap. In stage 3, sequences of detection boxes most likely to be associated with a single action instance, called action tubes, are constructed by solving two energy maximisation problems via dynamic programming. While in the first pass, action paths spanning the whole video are built by linking detection boxes over time using their class-specific scores and their spatial overlap, in the second pass, temporal trimming is performed by ensuring label consistency for all constituting detection boxes. We demonstrate the performance of our algorithm on the challenging UCF101, J-HMDB-21 and LIRIS-HARL datasets, achieving new state-of-the-art results across the board and significantly increasing detection speed at test time. We achieve a huge leap forward in action detection performance and report a 20% and 11% gain in mAP (mean average precision) on UCF-101 and J-HMDB-21 datasets respectively when compared to the state-of-the-art.

Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos

The reviewed paper presents a comprehensive framework for spatiotemporal detection and classification of concurrent actions in untrimmed video sequences. This framework is composed of three stages: appearance and motion detection, fusion of detections, and construction of action tubes. It leverages modern deep learning techniques and significantly enhances spatiotemporal action detection accuracy as well as computational efficiency across various challenging datasets.

The core of the proposed method capitalizes on region proposal networks (RPN) and Fast R-CNN networks built on the VGG-16 architecture to perform both action localization and classification in an end-to-end manner. The paper details a three-stage approach as follows:

1. Detection Stage: This involves the generation of region proposals through RPNs, taking both RGB and optical flow information as input. The extracted proposals are processed by Fast R-CNN networks to predict regressed bounding boxes along with classification scores for each action class.
2. Fusion Stage: The fusion of appearance and motion cues is strategically achieved by refining classification scores based on spatial overlaps and softmax probabilities. This strategy enhances the confidence of the detection boxes, leading to superior accuracy over separate appearance or motion models.
3. Action Tube Construction: The construction of action tubes is performed via dynamic programming, involving two passes. The first pass identifies optimal class-specific paths by linking boxes in time using scores and spatial overlap, while the second pass temporally trims the tubes, ensuring label consistency over time.

The paper reports exceeding previous state-of-the-art results on substantial benchmarks such as UCF-101, J-HMDB-21, and LIRIS-HARL, with the fused detection model showing notable improvement in mAP. The proposed framework is shown to be twice as fast in training and fivefold faster in detection compared to existing methods like those by Weinzaepfel et al. and Gkioxari and Malik, highlighting its computational resourcefulness and efficacy.

Implications and Future Directions:

• Practical Implications: The authors demonstrate enhanced speed and detection performance in real-world video datasets, which is promising for applications such as video surveillance, human-computer interaction, and automated video content analysis.
• Theoretical Contributions: The integration of single-stage deep learning architectures with late probability-based fusion offers a new paradigm in action recognition and localization, paving the way for more sophisticated and efficient spatiotemporal analysis models.
• Future Developments: The possibility of real-time implementation by further optimizing the framework for streaming video input is a natural progression. Additionally, exploring other deep architectures or advanced fusion strategies may yield further improvements in accuracy or computational efficiency.

Overall, this paper significantly contributes to the field of spatiotemporal action detection, emphasizing both accuracy and speed. Its novel approach to solving the action tube generation problem through multiple overlapping strategies indicates a constructive pathway for advanced video analysis techniques in multimedia and AI systems.