An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning

Haq, Hafiz Burhan Ul; Asif, Muhammad; Ahmad, Maaz Bin; Ashraf, Rehan; Mahmood, Toqeer

doi:10.1155/2022/7453744

Cited by 23 publications

(20 citation statements)

References 56 publications

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“…Hafiz Burhan Ul Haq et al [9] proposed a deep learning based system for tailored video summarization. The suggested framework facilitates video summarization based on the Object of Interest (OoI), such as individuals, aircraft, mobile devices, bicycles, and automobiles.…”

Section: Related Workmentioning

confidence: 99%

Image Caption Generation using Deep Learning For Video Summarization Applications

Inayathulla,

2024

IJACSA

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In the area of video summarization applications, automatic image caption synthesis using deep learning is a promising approach. This methodology utilizes the capabilities of neural networks to autonomously produce detailed textual descriptions for significant frames or instances in a video. Through the examination of visual elements, deep learning models possess the capability to discern and classify objects, scenarios, and actions, hence enabling the generation of coherent and useful captions. This paper presents a novel methodology for generating image captions in the context of video summarizing applications. DenseNet201 architecture is used to extract image features, enabling the effective extraction of comprehensive visual information from keyframes in the videos. In text processing, GloVe embedding, which is pre-trained word vectors that capture semantic associations between words, is employed to efficiently represent textual information. The utilization of these embeddings establishes a fundamental basis for comprehending the contextual variations and semantic significance of words contained within the captions. LSTM models are subsequently utilized to process the GloVe embeddings, facilitating the development of captions that keep coherence, context, and readability. The integration of GloVe embeddings with LSTM models in this study facilitates the effective fusion of visual and textual data, leading to the generation of captions that are both informative and contextually relevant for video summarization. The proposed model significantly enhances the performance by combining the strengths of convolutional neural networks for image analysis and recurrent neural networks for natural language generation. The experimental results demonstrate the effectiveness of the proposed approach in generating informative captions for video summarization, offering a valuable tool for content understanding, retrieval, and recommendation.

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Section: Related Workmentioning

confidence: 99%

Image Caption Generation using Deep Learning For Video Summarization Applications

Inayathulla,

2024

IJACSA

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“…This scheme summarized videos based on objects only, whereas events and privacy of objects was not considered. An object of interest (OoI) based video summarization was proposed by [20], which also used YOLO to detect OoI and compared the object given as input to the objects in dictionary. The frames that had the required objects were saved in a summarized video.…”

Section: Video Summarization Through Yolomentioning

confidence: 99%

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

et al. 2023

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The purpose of smart surveillance systems for automatic detection of road traffic accidents is to quickly respond to minimize human and financial losses in smart cities. However, along with the self-evident benefits of surveillance applications, privacy protection remains crucial under any circumstances. Hence, to ensure the privacy of sensitive data, European General Data Protection Regulation (EU-GDPR) has come into force. EU-GDPR suggests data minimisation and data protection by design for data collection and storage. Therefore, for a privacy-aware surveillance system, this paper targets the identification of two areas of concern: (1) detection of road traffic events (accidents), and (2) privacy preserved video summarization for the detected events in the surveillance videos. The focus of this research is to categorise the traffic events for summarization of the video content, therefore, a state-of-the-art object detection algorithm, i.e., You Only Look Once (YOLOv5), has been employed. YOLOv5 is trained using a customised synthetic dataset of 600 annotated accident and non-accident video frames. Privacy preservation is achieved in two steps, firstly, a synthetic dataset is used for training and validation purposes, while, testing is performed on real-time data with an accuracy from 55% to 85%. Secondly, the real-time summarized videos (reduced video duration to 42.97% on average) are extracted and stored in an encrypted format to avoid un-trusted access to sensitive event-based data. Fernet, a symmetric encryption algorithm is applied to the summarized videos along with Diffie–Hellman (DH) key exchange algorithm and SHA256 hash algorithm. The encryption key is deleted immediately after the encryption process, and the decryption key is generated at the system of authorised stakeholders, which prevents the key from a man-in-the-middle (MITM) attack.

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“…Brand image can increase brand trust levels and trigger brand impact, which in turn increases the level of brand loyalty [24] . Similarly, a number of researchers have performed simulations [25][26][27][28][29][30][31][32][33][34][35] . In order to maximize the significant economic benefits offered by brand equity as an interactive, market-based resource, businesses need to create brand trust [36] .…”

Section: Brand Trustmentioning

confidence: 99%

Enhancing customer satisfaction in e-commerce: The role of service quality and brand trust

Iqbal,

Tanveer,

Ul Haq

et al. 2023

Forum. Econ. Fin. Stud.

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Although online shopping has become more and more popular, keeping clients is still a big problem. Satisfaction, which is mostly dependent on comprehending and establishing trust with customers, is what determines whether or not a business can retain its customers. In the ever-changing world of e-commerce, this study examines the relationship between consumer loyalty, happiness, and service quality, with a focus on brand trust. The findings indicate that several aspects of service quality, including order processing, speed, convenience, and quality of communication from couriers, have a big impact on how satisfied customers are with their online purchases. The most powerful element influencing pleasure, however, turned out to be ease. The relationship’s key mediator is brand trust. As a result, consumer loyalty is increased and uncertainty is decreased. In particular, in developing e-commerce countries such as Pakistan, the study emphasizes how important it is to enhance consumer happiness, service quality, and brand credibility. By stressing the mediating function of brand trust and highlighting the dynamics in emerging nations, this research adds to the body of current work. It emphasizes how crucial trust and quality customer service are to creating long-lasting, customer-focused e-commerce enterprises. While acknowledging its limits, the study provides useful insights and avenues for further cross-country research.

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An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning

Cited by 23 publications

References 56 publications

Image Caption Generation using Deep Learning For Video Summarization Applications

Image Caption Generation using Deep Learning For Video Summarization Applications

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

Enhancing customer satisfaction in e-commerce: The role of service quality and brand trust

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