Video Summarization Research Articles

The Future of Smart Home Security: Generative AI and LLMs for Intelligent Event Detection and Personalized Notifications

Abstract—Smart home security cameras are becoming more common, but their usefulness can be diminished by notification fatigue from too many alerts about minor incidents. This paper examines the gaps of existing event detection and notification systems in security cameras and then recommends using Generative AI and Large Language Models (LLMs) to add intelligence which would improve user experience. Generative AI can be leveraged to classify events more accurately and assist with anomaly detection. LLMs can further be used to create notifications that are tailored to the context and personalized to users behavior, helping to reduce notification fatigue and provide meaningful user alerts. The paper also looks into wider applications of these technologies to add intelligence and improve other related experiences like automated video summarization, proactive security measures, and improved privacy controls. The integration of Generative AI and LLMs with smart home security camera systems advances the smart cameras capabilities and offers enhanced security, personalized user experiences. Keywords—Smart home security, Generative AI, Large Language Models (LLMs), Event detection, Anomaly detection, Notification fatigue, Context-aware notifications, Personalized security, Reinforcement Learning from Human Feedback (RLHF), Internet of Things (IoT).

An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird

Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications.

Hierarchical multi‐modal video summarization with dynamic sampling

AbstractPrevious video summarization methods often neglected inter‐frame variations during the preprocessing stage. Sampling repeated frames can lead to information redundancy, while missing key frames can result in deviations in semantic comprehension and inaccuracies in the generated summaries. This work proposes a dynamic sampling module that leverages frame‐level motion information to alleviate these issues. The module conducts high‐frequency sampling during intervals with significant changes, allowing for a finer capture of details. Combined with a hierarchical multi‐modal structure, it integrates shot‐level visual and textual information to enhance the semantic understanding of video clips and improve the accuracy of the summarized content. Extensive experiments on benchmark datasets SumMe and TVSum demonstrate the effectiveness of the proposed method.

Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest

Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest

Optimized deep learning enabled lecture audio video summarization

Video summarization plays an important role in multiple applications by compressing lengthy video content into compressed representation. The purpose is to present a fine-tuned deep model for lecture audio video summarization. Initially, the input lecture audio-visual video is taken from the dataset. Then, the video shot segmentation (slide segmentation) is done using the YCbCr space colour model. From each video shot, the audio and video within the video shot are segmented using the Honey Badger-based Bald Eagle Algorithm (HBBEA). The HBBEA is obtained by combining the Bald Eagle Search (BES) and Honey Badger Algorithm (HBA). The DRN training is executed by HBBEA to select the finest DRN weights. The relevant video frames are merged with the audio. The proposed HBBEA-based DRN outperformed with a better F1-Score of 91.9 %, Negative predictive value (NPV) of 89.6 %, Positive predictive value (PPV) of 90.7 %, Accuracy of 91.8 %, precision of 91 %, and recall of 92.8 %.

Unleashing the Power of Contrastive Learning for Zero-Shot Video Summarization.

Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing. Past efforts have invariantly involved training summarization models with annotated summaries or heuristic objectives. In this work, we reveal that features pre-trained on image-level tasks contain rich semantic information that can be readily leveraged to quantify frame-level importance for zero-shot video summarization. Leveraging pre-trained features and contrastive learning, we propose three metrics featuring a desirable keyframe: local dissimilarity, global consistency, and uniqueness. We show that the metrics can well-capture the diversity and representativeness of frames commonly used for the unsupervised generation of video summaries, demonstrating competitive or better performance compared to past methods when no training is needed. We further propose a contrastive learning-based pre-training strategy on unlabeled videos to enhance the quality of the proposed metrics and, thus, improve the evaluated performance on the public benchmarks TVSum and SumMe.

5G‐based video summarization: An analysis from a methodological point of view

AbstractSurveillance is one of the fast‐growing applications used for monitoring and watching people, objects, or the environment to collect information and provide security. The surveillance data is in video form, and analyzing large video is challenging because it is essential to do efficient video streaming online. Video summarization comprises selecting, extracting, and aggregating keyframes for creating a synopsis, which is challenging. Though several methods have been proposed for video summarization, most are inconsistent, poor in processing and delivering video content, and do not focus on solving the root problems interlinked with efficient streaming. Thus, video streaming applications require an efficient video summarization model that can overcome existing issues and challenges and improve the overall quality of service integrated with the advanced technology of 5G. This paper has aimed to discuss various methods, approaches, and technologies used for video summarization to design a better model. It also presents various learning models and a taxonomy of available methods and provides a detailed review. The summary of the model used evaluates its outcome and the existing methods for potential future research works. The proposed approach is compared with existing ones to prove the model's efficiency. The result shows that the proposed model achieved a 62.3 and 52.3 F1 score summarizing the TVSum and SumMe datasets, respectively.

A novel approach for long-term secure storage of domain independent videos

Long-term protection of multimedia contents is a complex task, especially when the video has critical elements. It demands sophisticated technology to ensure confidentiality. In this paper, we propose a blended approach which uses proactive visual cryptography scheme along with video summarization techniques to circumvent the aforementioned issues. Proactive visual cryptography is used to protect digital data by updating periodically or renewing the shares, which are stored in different servers. And, video summarization schemes are useful in various scenarios where memory is a major concern. We use a domain independent scheme for summarizing videos and is applicable to both edited and unedited videos. In our scheme, the visual continuity of the raw video is preserved even after summarization. The original video can be reconstructed through the shares using auxiliary data, which was generated during video summarization phase. The mathematical studies and experimental results demonstrate the applicability of our proposed method.

A new approach to video summary generation

This paper describes a video summary approach for an actual video file that provides a rapid way to assess its information. This is a difficult task to identify relevant or informative sections. When the original video demands knowledge of its content, we build a video summary via extracting in-depth characteristics of each original video. Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant information provides a concise and accurate representation of the original video clips by providing the most representative summary with increased interest through the application of a matching function between the current and prior frames. The primary goal of this work is to give a strategy for recording video clips using security cameras and converting them into a short video to provide a clear analysis of the video by reducing excess and extracting the key video contents into the real video. In a lengthy video clip, viewers may not have enough time to see the full thing. The viewer may be interested in seeing a specific topic under the label "Important". It may be composed of pieces that may be shared on social media and communicate the semantics of long videos, particularly video snippets that must be submitted to Internet. To browse, will be needed a network with adequate bandwidth. That is, non-affiliated video streaming, indexing, and retrieval of video content makes it valuable to viewers.

Review Paper on Deep RL for Video Summarization

In recent years, the explosive growth of video content has heightened the need for efficient summarization techniques to distill lengthy videos into concise, informative summaries. Traditional approaches to video summarization often rely on heuristic-based methods or supervised learning techniques, which can be limited by their reliance on predefined features or extensive labeled datasets. To address these limitations, this paper explores the application of Deep Reinforcement Learning (DRL) for video summarization. DRL offers a dynamic framework where an agent learns to optimize summarization strategies through interaction with the video content, enabling adaptive and context-aware summarization. We propose a novel DRL-based framework that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract and represent temporal features from video frames. Actor- critic architecture is utilized, where the actor generates candidate summaries and the critic evaluates their quality based on a reward function designed to balance in formativeness and brevity. We introduce a new reward function that incorporates both content relevance and diversity to encourage the generation of summaries that effectively capture key moments and maintain narrative coherence. Experimental results on benchmark video datasets demonstrate that our DRL-based approach significantly outperforms traditional methods in terms of both summary quality and user satisfaction. The proposed method not only achieves state-of-the-art performance but also offers greater flexibility and adaptability to diverse video content. This work highlights the potential of DRL in advancing video summarization and opens avenues for future research in optimizing video content extraction and representation

Saliency-based video summarization for face anti-spoofing

With the growing availability of databases for face presentation attack detection, researchers are increasingly focusing on video-based face anti-spoofing methods that involve hundreds to thousands of images for training the models. However, there is currently no clear consensus on the optimal number of frames in a video to improve face spoofing detection. Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing detection that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency. In particular, saliency information is extracted from the differences between the Laplacian and Wiener filter outputs of the source images, enabling the identification of the most visually salient regions within each frame. Subsequently, the source images are decomposed into base and detail images, enhancing the representation of the most important information. Weighting maps are then computed based on the saliency information, indicating the importance of each pixel in the image. By linearly combining the base and detail images using the weighting maps, the method fuses the source images to create a single representative image that summarizes the entire video. The key contribution of the proposed method lies in demonstrating how visual saliency can be used as a data-centric approach to improve the performance and efficiency for face presentation attack detection. By focusing on the most salient images or regions within the images, a more representative and diverse training set can be created, potentially leading to more effective models. To validate the method’s effectiveness, a simple CNN–RNN deep learning architecture was used, and the experimental results showcased state-of-the-art performance on four challenging face anti-spoofing datasets.

GAT-Based Bi-CARU with Adaptive Feature-Based Transformation for Video Summarisation

Nowadays, video is a common social media in our lives. Video summarisation has become an interesting task for information extraction, where the challenge of high redundancy of key scenes leads to difficulties in retrieving important messages. To address this challenge, this work presents a novel approach called the Graph Attention (GAT)-based bi-directional content-adaptive recurrent unit model for video summarisation. The model makes use of the graph attention approach to transform the visual features of interesting scene(s) from a video. This transformation is achieved by a mechanism called Adaptive Feature-based Transformation (AFT), which extracts the visual features and elevates them to a higher-level representation. We also introduce a new GAT-based attention model that extracts major features from weight features for information extraction, taking into account the tendency of humans to pay attention to transformations and moving objects. Additionally, we integrate the higher-level visual features obtained from the attention layer with the semantic features processed by Bi-CARU. By combining both visual and semantic information, the proposed work enhances the accuracy of key-scene determination. By addressing the issue of high redundancy among major information and using advanced techniques, our method provides a competitive and efficient way to summarise videos. Experimental results show that our approach outperforms existing state-of-the-art methods in video summarisation.

Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques

One of the most reliable information sources is video, and in recent years, online and offline video consumption has increased to an unprecedented degree. One of the main difficulties in extracting information from videos is that unlike images, where information can be gleaned from a single frame, a viewer must watch the entire video in order to comprehend the context. In this work, we try to use various algorithmic techniques, such as deep neural networks and local features, in conjunction with a variety of clustering techniques, to find an efficient method of extracting interesting key frames from videos to summarize them. Video summarization plays a major role in video indexing, browsing, compression, analysis, and many other domains. One of the fundamental elements of video structure analysis is key frame extraction, which pulls significant frames out of the movie. An important frame from a video that may be used to summarize videos is called a key frame. We provide a technique that leverages convolutional neural networks in our suggested model, static video summarization, and key frame extraction from movies.

Global–local spatio-temporal graph convolutional networks for video summarization

Video summarization aims to create concise and accurate summary to enable users to quickly grasp the key content of the original video for facilitating efficient video browsing. Most existing video summarization methods mainly employ recurrent neural networks to capture long-term dependencies in videos, yielding remarkable results. Nevertheless, these methods overlook the potential spatial features inside the video when modeling the video. To tackle this issue, we introduce a global–local spatio-temporal graph convolutional networks for video summarization (GL-STGCN). Initially inspired by the concept of 3-D convolution, we segment the video into non-overlapping segments to capture localized spatial features of sequential frames. Subsequently, a spatial graph is constructed for each segment, with a fixed time interval between neighboring spatial graphs. Then we use the pooling method to randomly delete the redundant nodes in the graph. We then leverage a temporal gating convolutional network to extract the global temporal relationships within the video. Employing the spatial features, a spatial graph convolutional network is utilized to capture the spatial connections among frames. As the graph node information evolves, the node features furnish a more precise depiction of the video content. Consequently, we employ the temporal gating convolutional network once more to refine the global temporal relations within the video. Extensive experiments on two public datasets are conducted in this paper, showing that our proposed method outperforms most state-of-the-art video summarization methods in terms of performance. Experimental results demonstrate the effectiveness of integrating global temporal and local spatial relationships.

Feature fusion-based video summarization using SegNetSN

Feature fusion-based video summarization using SegNetSN

Behavioral profiling for adaptive video summarization: From generalization to personalization

In today's world of managing multimedia content, dealing with the amount of CCTV footage poses challenges related to storage, accessibility and efficient navigation. To tackle these issues, we suggest an encompassing technique, for summarizing videos that merges machine-learning techniques with user engagement. Our methodology consists of two phases, each bringing improvements to video summarization. In Phase I we introduce a method for summarizing videos based on keyframe detection and behavioral analysis. By utilizing technologies like YOLOv5 for object recognition, Deep SORT for object tracking, and Single Shot Detector (SSD) for creating video summaries. In Phase II we present a User Interest Based Video summarization system driven by machine learning. By incorporating user preferences into the summarization process we enhance techniques with personalized content curation. Leveraging tools such as NLTK, OpenCV, TensorFlow, and the EfficientDET model enables our system to generate customized video summaries tailored to preferences. This innovative approach not only enhances user interactions but also efficiently handles the overwhelming amount of video data on digital platforms. By combining these two methodologies we make progress in applying machine learning techniques while offering a solution to the complex challenges presented by managing multimedia data.

Static video summarization based on genetic algorithm and deep learning approach

Static video summarization based on genetic algorithm and deep learning approach

Effective Video Summarization Using Channel Attention-Assisted Encoder–Decoder Framework

A significant number of cameras regularly generate massive amounts of data, demanding hardware, time, and labor resources to acquire, process, and monitor. Asymmetric frames within videos pose a challenge to automatic summarization of videos, making it challenging to capture key content. Developments in computer vision have accelerated the seamless capture and analysis of high-resolution video content. Video summarization (VS) has garnered considerable interest due to its ability to provide concise summaries of lengthy videos. The current literature mainly relies on a reduced set of representative features implemented using shallow sequential networks. Therefore, this work utilizes an optimal feature-assisted visual intelligence framework for representative feature selection and summarization. Initially, the empirical analysis of several features is performed, and ultimately, we adopt a fine-tuning InceptionV3 backbone for feature extraction, deviating from conventional approaches. Secondly, our strategic encoder–decoder module captures complex relationships with five convolutional blocks and two convolution transpose blocks. Thirdly, we introduced a channel attention mechanism, illuminating interrelations between channels and prioritizing essential patterns to grasp complex refinement features for final summary generation. Additionally, comprehensive experiments and ablation studies validate our framework's exceptional performance, consistently surpassing state-of-the-art networks on two benchmarks (TVSum and SumMe) datasets.

Unsupervised video summarization with adversarial graph-based attention network

Video summarization aims to select a subset of video segments that best capture the video storyline. Our study seeks to train an encoder to transform the raw frame features extracted from pre-trained CNN models into representations that embody importance and guide the selection of the video segment. Our main idea is to use graph modeling and attention mechanisms to train the encoder adversarially. The graph representation enables the model to learn the relationship among frames, revealing the intrinsic structure of a video. The attention mechanism allows the model to capture the magnitude of these relationships. In the proposed model, an attention-based encoder is trained using a graph-based generator that reconstructs videos using the encoded features and a discriminator that guides the generator, distinguishing the original and reconstructed video. Thus, by leveraging graph attention and refining mechanisms, the proposed model offers distinct advantages over existing methods, including enhanced summarization accuracy, improved preservation of temporal coherence, and the ability to capture complex semantic linkages within video content. These advancements are substantiated through a comprehensive ablation study, which demonstrates the efficacy of our model using various evaluation metrics — Kendall and Spearman coefficients. The proposed model is evaluated on TVSum and SumMe datasets and achieves results on par with supervised models that used similar encoders and achieved state-of-the-art results compared to other unsupervised models.

Video Summarization using Attention Mechanisms and CNN

This Video summarization is a vital task in multimedia analysis, especially given the vast volume of video data in the digital age. Although deep learning methods have been extensively studied for this purpose, they often face challenges in efficiently processing long-duration videos. This paper tackles the issue of unsupervised video summarization by introducing a novel approach that selects a sparse subset of video frames to optimally represent the original video. The core concept involves training a deep summarizer network within a generative adversarial framework, which includes an autoencoder LSTM network as the summarizer and another LSTM network as the discriminator. The summarizer LSTM is designed to select key video frames and reconstruct the input video from these selected frames. Concurrently, the discriminator LSTM's role is to differentiate between the original video and its reconstruction. Through adversarial training between the summarizer and discriminator, combined with sparsity regularization, the network learns to produce optimal video summaries without requiring labeled data. Evaluations on several benchmark datasets indicate that this method delivers competitive performance compared to fully supervised state-of-the-art techniques, highlighting its effectiveness in unsupervised video summarization. Key Words: Event summarization, Critical information in videos, Surveillance systems ,Video analysis, Multimedia analysis, Deep learning, Unsupervised learning, Autoencoder LSTM, Long short-term memory network (LSTM)