Video Captioning in Surveillance Videos

Introduction

Video Captioning (VC) generates a suitable caption for the given query frames of a video. The focus of VC is understanding the contextual meaning of visual contents in a video and then describing them in semantically and syntactically correct descriptions in the natural language. The most salient part of a video is described in a sentence to increase the visual understanding of humans by saving their time. The concept of Video Captioning has emerged from Image Captioning where both Computer Vision and NLP domains were fused. This merging is now further shifted to Video Captioning to enhance the better visual understanding on video level. Sequence to Sequence nature of Video Captioning problem follows Encoder Decoder Architecture makes it complex as well as challenging due to the sequence of pixel frames on one end as an input and sequence of words in natural language on another end as output. Traditional approaches used retrieval based and template based captioning but after the deep learning era, human like captions are generated automatically. Latest state of the art Transformer based approaches such as ClipBert , Swinbert and Clip4Caption designed for Video Captioning produce outstanding performance. VC has many applications like navigation guidance for blind persons, video-based optimized search engines, visual context understanding, auto news captioning, early babyhood education, video description on an e-commerce site, and video indexing. BLEU, ROUGH-L, CIDEr, METEOR & SODA C are evaluation measures used to evaluate the performance of deep learning and transfer learning, attention-based approaches, and transformers on video captioning. In the context of surveillance videos, accurate captioning can significantly enhance monitoring, security, and analysis processes. By automating the description of activities, behaviors, and events, video captioning systems can assist security personnel in quickly understanding and responding to incidents. 

Importance of Video Captioning in Surveillance

• Enhanced Monitoring: 

Automatically generated captions provide real-time insights without manual observation.

• Incident Analysis: 

Facilitates quick review and documentation of events for investigation.

• Accessibility:

Makes surveillance content accessible to individuals with hearing impairments.

• Data Management: 

Simplifies indexing and searching through vast amounts of video data

Datasets For Video Captioning In Surveillance Videos

• UCF Dataset

The UCF dataset, developed by the University of Central Florida, is a widely used benchmark in video action recognition and captioning tasks. It contains a large collection of YouTube videos spanning various action categories.

Key Features:

• Annotations: 

Provides labeled data with action descriptions.

• High Variability: 

Includes different camera angles, lighting conditions, and environments, making it suitable for surveillance applications.

Applications:

• Action Recognition: 

Identifying and classifying actions within videos.

• Video Captioning: 

Generating descriptive captions based on detected actions.

• Event Detection:

Recognizing complex events involving multiple actions.

Other Relevant Datasets

There are several datasets available for video captioning research, each offering a diverse range of video content and associated captions. Here are some popular datasets used in video captioning research: 

• MSVD (Microsoft Research Video Description):

A dataset with short video clips sourced from YouTube, each accompanied by multiple human-annotated descriptions.

• MSR-VTT (Microsoft Research Video to Text):

An extension of MSVD, MSR-VTT is a larger dataset containing over 10,000 video clips and associated descriptions.

• ActivityNet Captions:

ActivityNet Captions provides a large-scale dataset with untrimmed videos and multiple human-annotated captions per video. It covers a wide range of activities.

• Charades:

The Charades dataset consists of videos recorded by actors performing daily activities. It is designed for both action recognition and video captioning tasks.

• YouTube2Text:

This dataset consists of videos from YouTube with corresponding English descriptions. It is suitable for training and evaluating video captioning models.

• YouCookII:

YouCookII contains videos capturing the cooking process, making it suitable for video captioning related to sequential activities

Challenges

Video Captioning is a complex task that involves addressing several challenges due to the dynamic nature of video content and the need to generate accurate and contextually relevant captions. Some of the key challenges in video captioning research include:

• Temporal Misalignment

• Ambiguity and Diversity

• Long-Term Dependency

• Object Recognition and Tracking

• Language Ambiguity

• Data Scarcity and Annotation Challenges

• Low Quality Surveillance Videos

References

1. W. Ji et al., "An Attention Based Dual Learning Approach for Video Captioning," Applied Soft Computing, vol. 117, pp. 108332, Mar. 2022, doi: 10.1016/j.asoc.2021.108332.

2. V. Jain, F. Al-Turjman, G. Chaudhary, D. Nayar, V. Gupta, and A. Kumar, "Video captioning: a review of theory, techniques and practices," Multimedia Tools and Applications, vol. 81, no. 25, pp. 35619-35653, 2022.

3. G. Rafiq, M. Rafiq, and G. S. Choi, "Video description: A comprehensive survey of deep learning approaches," Artificial Intelligence Review, pp. 1-80, 2023.

4.  J. Lei, L. Li, L. Zhou, Z. Gan, T. L. Berg, M. Bansal, and J. Liu, "Less is more: Clipbert for video-and-language learning via sparse sampling," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7331-7341, 2021.

5. K. Lin, L. Li, C. C. Lin, F. Ahmed, Z. Gan, Z. Liu, and L. Wang, "Swinbert: End-to-end transformers with sparse attention for video captioning," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17949-17958, 2022.