Real-time tracking of low-resolution vehicles for wide-area persistent surveillance

Keck, Mark; Galup, L.; Stauffer, Chris

doi:10.1109/wacv.2013.6475052

Cited by 49 publications

(33 citation statements)

References 10 publications

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“…Featurebased image registration (see section II) was used to align consecutive frames. To compare both detection and tracking performance with existing papers, we additionally create six sub-videos including different Areas Of Interest (AOI 01, 02, 03, 34, 40, 41 in [3], [2], [27], [23]). As all these papers did not fully specify the details about the areas of interest, we use our pre-processing architecture to generate sub-videos which attempt to cover similar regions to those specified in these papers.…”

Section: Resultsmentioning

confidence: 99%

“…In order to measure the tracker, we used the definitions and metrics in [23], [27]. The estimated track will be referred to as 'track' and the ground-truth target The precisions and recalls on different areas of interest using the proposed detector.…”

Section: A Evaluation Methodsmentioning

confidence: 99%

“…Both situations influence the result of background subtraction. As suggested in [23], we use a box filter to compensate for these brightness fluctuations. We perceive that this removes false alarms without reducing the probability of detection significantly.…”

Section: Moving Object Detection a Background Subtractionmentioning

confidence: 99%

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Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

Zhou

Maskell

2019

Preprint

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This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated. Identifying the objects only using foreground appearance is difficult since a 100−pixel vehicle is hard to distinguish from objects comprising the background. Our approach is based on background subtraction as an efficient and unsupervised method that is able to output the shape of objects. In order to reliably detect low contrast and small objects, we configure the background subtraction to extract foreground regions that might be objects of interest. While this dramatically increases the number of false alarms, a Convolutional Neural Network (CNN) considering both spatial and temporal information is then trained to reject the false alarms. In areas with heavy traffic, the background subtraction yields merged detections. To reduce the complexity of multi-target tracker needed, we train another CNN to predict the positions of multiple moving objects in an area. Our approach shows competitive detection performance on smaller objects relative to the state-of-the-art. We adopt a GM-PHD filter to associate detections over time and analyse the resulting performance.

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Section: Resultsmentioning

confidence: 99%

Section: A Evaluation Methodsmentioning

confidence: 99%

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Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

Zhou

Maskell

2019

Preprint

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“…Construction of the background model also takes several frames and increases algorithmic complexity and memory utilization. Frame differencing-based methods [28]- [32] also rely on registered images to detect motion. Unlike background subtraction, frame differencing-based methods rely on fewer consecutive images.…”

Section: Related Workmentioning

confidence: 99%

HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

et al. 2022

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Wide Area Motion Imagery (WAMI) yields high resolution images with a large number of extremely small objects. Target objects have large spatial displacements throughout consecutive frames. This nature of WAMI images makes object tracking and detection challenging. In this paper, we present our deep neural network-based combined object detection and tracking model, namely, Heat Map Network (HM-Net). HM-Net is significantly faster than state-of-the-art frame differencing and background subtractionbased methods, without compromising detection and tracking performances. HM-Net follows object centerbased joint detection and tracking paradigm. Simple heat map-based predictions support unlimited number of simultaneous detections. The proposed method uses two consecutive frames and the object detection heat map obtained from the previous frame as input, which helps HM-Net monitor spatio-temporal changes between frames and keep track of previously predicted objects. Although reuse of prior object detection heat map acts as a vital feedback-based memory element, it can lead to unintended surge of false positive detections. To increase robustness of the method against false positives and to eliminate low confidence detections, HM-Net employs novel feedback filters and advanced data augmentations. HM-Net outperforms state-of-the-art WAMI moving object detection and tracking methods on WPAFB dataset with its 96.2% F1 and 94.4% mAP detection scores, while achieving 61.8% mAP tracking score on the same dataset.This performance corresponds to an improvement of 2.1% for F1, 6.1% for mAP scores on detection, and 9.5% for mAP score on tracking over state-of-the-art.

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“…This method utilized vehicle behavior model from road structure to detect and track in wide area. Keck et al proposed a real-time system for detecting and tracking moving objects from aerial images [7]. These papers focused on vehicle tracking.…”

Section: Previous Workmentioning

confidence: 99%

Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis

Iwashita

Ryoo

Fuchs³

et al. 2013

Procedings of the British Machine Vision Conference 2013

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We propose a novel method for recognizing people in aerial surveillance videos. Aerial surveillance images cover a wide area at low resolution. In order to detect objects (e.g., pedestrians) from such videos, conventional methods either utilize appearance information from raw videos or extract blob information from background subtraction results. However, people seen in low resolution images have less appearance information, and hence are very difficulty to classify based on their appearance or blob size. In addition, due to heavy camera movements caused by aerial vehicle ego-motion and wind, the system is expected to generate many noisy false detections including parallax. The idea presented in this paper is to detect and classify objects from aerial videos based on their motion: we analyze a trajectory of each object candidate, deciding whether it is a person-of-interest or simple noise based on how it moved. After objects are tracked by a Kalman filter-based tracking, we represent their motion as multi-scale histograms of 'orientation changes', which efficiently captures movements displayed by objects. Random forest classifiers are applied to our new representation to make the decision. The experimental results illustrate that our approach recognizes objects-of-interest (i.e., humans) even when there exist a large number of false detection/tracking, and it does it more reliably compared to the approaches with previous paradigm.

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Real-time tracking of low-resolution vehicles for wide-area persistent surveillance

Cited by 49 publications

References 10 publications

Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis

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