Moving Objects Segmentation Based on DeepSphere in Video Surveillance

Ammar, Sirine; Bouwmans, Thierry; Zaghden, Nizar; Néji, Mahmoud

doi:10.1007/978-3-030-33723-0_25

Cited by 12 publications

(7 citation statements)

References 31 publications

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“…Segmentation plays an important role in many applications seen today, including medical imaging analysis (tissue volume measurement, tumor identification), autonomous vehicles (ego-lane extraction, pedestrian detection), video surveillance, and augmented reality [30][31][32][33][34]. In recent years, with widespread availability and accessibility of higher computational power, deep learning algorithms enabled the development of segmentation algorithms that significantly outperformed its handcrafted-feature-based predecessors.…”

Section: Related Work 21 State-of-the-art Semantic Segmentationmentioning

confidence: 99%

MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms

2021

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We present MultEYE, a traffic monitoring system that can detect, track, and estimate the velocity of vehicles in a sequence of aerial images. The presented solution has been optimized to execute these tasks in real-time on an embedded computer installed on an Unmanned Aerial Vehicle (UAV). In order to overcome the limitation of existing object detection architectures related to accuracy and computational overhead, a multi-task learning methodology was employed by adding a segmentation head to an object detector backbone resulting in the MultEYE object detection architecture. On a custom dataset, it achieved 4.8% higher mean Average Precision (mAP) score, while being 91.4% faster than the state-of-the-art model and while being able to generalize to different real-world traffic scenes. Dedicated object tracking and speed estimation algorithms have been then optimized to track reliably objects from an UAV with limited computational effort. Different strategies to combine object detection, tracking, and speed estimation are discussed, too. From our experiments, the optimized detector runs at an average frame-rate of up to 29 frames per second (FPS) on frame resolution 512 × 320 on a Nvidia Xavier NX board, while the optimally combined detector, tracker and speed estimator pipeline achieves speeds of up to 33 FPS on an image of resolution 3072 × 1728. To our knowledge, the MultEYE system is one of the first traffic monitoring systems that was specifically designed and optimized for an UAV platform under real-world constraints.

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Section: Related Work 21 State-of-the-art Semantic Segmentationmentioning

confidence: 99%

MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms

2021

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“…Such a network can serve as both an adaptive model of the background in a video sequence and a Bayesian classifier of background or foreground pixels. In [24], Ammar et al . proposed an approach to detect and then segment moving objects based on deep NNs.…”

Section: Related Workmentioning

confidence: 99%

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Ammar

Bouwmans

Zaghden

et al. 2020

IET Image Processing

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“…With recent progress in object detection and facial classification, video surveillance systems incorporating both object detection and facial recognition have become more prevalent [22]. Motion detection-based methods are used for abandoned object detection in surveillance system [23], the method consists of background subtraction techniques, followed by optical flow analysis techniques and temporal differences technique [23].…”

Section: Introductionmentioning

confidence: 99%

Abandoned Object Detection and Classification Using Deep Embedded Vision

Qasim,

Abbas,

Ali

et al. 2024

IEEE Access

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One indispensable element within security systems deployed at public venues such as airports, bus stops, train stations, and marketplaces is video surveillance. The evolution of more robust and efficient automated technological solutions for video surveillance is imperative. In light of the escalating global threat of terrorist attacks in recent years, any unattended object in public areas is treated as potentially suspicious. Ensuring the protection of individuals in these public spaces necessitates the implementation of safety measures. The intricacies of surveillance recordings introduce challenges when it comes to identifying abandoned or removed objects, owing to factors like occlusion, abrupt lighting changes, and other variables. This paper proposes a novel two-stage method for identifying and locating stationary objects in public settings. The first stage uses a sequential model to capture temporal features and detect potential abandoned objects within the monitored area. When the sequential model detects such an object, it triggers a subsequent phase. The second stage uses YOLOv8l model to precisely locate the detected objects. YOLOv8l is renowned for its ability to accurately pinpoint object locations within the surveillance scene. The proposed method achieves remarkable accuracy rates of 99.20% and 99.70% on combined PETS 2006 and ABODA datasets, respectively, effectively localizing the target object. This achievement not only underscores the model's precision in accurately pinpointing the object's position within the given context but also establishes its superiority over other existing models. By integrating these two stages, our method provides an effective solution for enhancing the detection of abandoned objects in public spaces, contributing to improved security and safety measures.

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Moving Objects Segmentation Based on DeepSphere in Video Surveillance

Cited by 12 publications

References 31 publications

MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms

MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Abandoned Object Detection and Classification Using Deep Embedded Vision

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