A Robust Framework for Object Detection in a Traffic Surveillance System

Akhtar, Malik Javed; Mahum, Rabbia; Butt, Faisal Shafique; Amin, Rashid; El-Sherbeeny, Ahmed M.; Lee, Seongkwan Mark; Shaikh, Sarang

doi:10.3390/electronics11213425

Cited by 32 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Testing on four traffic video scenes achieved around 90% accuracy. Akhtar et al improved YOLOv2 for precise tiny object detection in surveillance videos [109]. They used DenseNet-201 for compact feature extraction, achieving an average precision of 97.51% in vehicle detection and recognition, outperforming other methods.…”

Section: Pedestrian and Traffic Detectionmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

View full text Add to dashboard Cite

From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance systems to seamlessly integrating with augmented reality, object detection algorithms stand as the cornerstone in unraveling the complexities of the visual world. Tracing the trajectory from conventional region-based methods to the latest neural network architectures reveals a technological renaissance where algorithms metamorphose into digital artisans. However, this journey is not without hurdles, prompting researchers to grapple with real-time detection, robustness in varied environments, and interpretability amidst the intricacies of deep learning. The allure of addressing issues such as occlusions, scale variations, and finegrained categorization propels exploration into uncharted territories, beckoning the scholarly community to contribute to an ongoing saga of innovation and discovery. This research offers a comprehensive panorama, encapsulating the applications reshaping our digital reality, the advancements pushing the boundaries of perception, and the open issues extending an invitation to the next generation of visionaries to explore uncharted frontiers within object detection.

show abstract

Section: Pedestrian and Traffic Detectionmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Deep learning-based models perform better for object localization and classification problems than machine learningbased models [19]. Although there have been various significant applications of machine learning-based methods, on the other side, they are based on complex codes and are less efficient due to their ample processing time.…”

Section: Figure 1: Different Images Of Face Emotions [13]mentioning

confidence: 99%

Facial Emotions Detection using an Efficient Neural Architecture Search Network

Islam

Mahum

Al-Salman

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Facial emotion detection is a challenging task that deals with emotion recognition. It has applications in various domains, such as behavior analysis, surveillance systems and human-computer interaction (HCI). Numerous studies have been implemented to detect emotions, including classical machine learning algorithms and advanced deep learning algorithms. For the machine learning algorithm, the hand-crafted feature needs to be extracted, which is a tiring task and requires human effort. Whereas in deep learning models, automated feature extraction is employed from samples. Therefore, in this study, we have proposed a novel and efficient deep learning model based on Neural Architecture Search Network utilizing superior artificial networks such as RNN and child networks. We performed the training utilizing the FER 2013 dataset comprising seven classes: happy, angry, neutral, sad, surprise, fear, and disgust. Furthermore, we analyzed the robustness of the proposed model on CK + datasets and comparing with existing techniques. Due to the implication of reinforcement learning in the network, most representative features are extracted from the sample network. It extracts all key features without losing the key information. Our proposed model is based on one stage classifier and performs efficient classification. Our technique outperformed the existing models attaining an accuracy of 98.14%, recall of 97.57%, and precision of 97.84%.

show abstract

“…[28]. Typically, the traditional CNN algorithms draw a thick collection of visual physiognomy from the information of all former slices to boost object identi cation accuracy [29]. However, due to the gradient vanishing problem in the learning phase, when the network thickness is increased, the algorithms with these types of design con gurations experience a signi cant performance impact [30].…”

Section: Base Resnet-18mentioning

confidence: 99%

An Efficient Deepfake Detection Using Robust Deep Learning Approch

Qadir

Mahum

El-Meligy

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The creation and manipulation of synthetic images have evolved rapidly, creating serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from following on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake video and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++.) We achieved 96.23% accuracy when using the FF + + digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF + + and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.

show abstract

A Robust Framework for Object Detection in a Traffic Surveillance System

Cited by 32 publications

References 41 publications

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Facial Emotions Detection using an Efficient Neural Architecture Search Network

An Efficient Deepfake Detection Using Robust Deep Learning Approch

Contact Info

Product

Resources

About