Adaptive automatic tracking, learning and detection of any real time object in the video stream

Nemade, Bhushan; Bharadi, Vinayak Ashok

doi:10.1109/confluence.2014.6949039

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…In order to save computational time, a strategy to combine both detection and tracking was studied. This topic was initially analysed in [25] and [26], with encouraging results. The former proposed a detection process carried out after a fixed number of frames.…”

Section: Detector-tracking Integrationmentioning

confidence: 99%

Adaptive Detection Tracking System for Autonomous UAV Maritime Patrolling

Panico

Fragonara

Al‐Rubaye

2020

2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace)

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Nowadays, Unmanned Aerial Vehicles (UAVs) are considered reliable autonomous systems suitable for several autonomous applications, especially for target detection and tracking. Although significant developments were achieved in object detection systems over the last decades using the deep learning technique known as Convolutional Neural Networks (CNN), there are still research gaps in this area. In this paper, we present a new object detection tracking algorithm that can apply on low power consuming processing boards. In particular, we analysed a specific application scenario in which a UAV patrols coastlines and autonomously classifies different kind of marine objects. Current state of the art solutions propose centralised architectures or flying systems with human in the loop, making the whole system poorly efficient and not scalable. On the contrary, applying Artificial Intelligence (AI) detection system that runs on commercial Graphics Processing Units (GPUs) makes UAVs potentially more efficient than humans (especially for dull tasks like coastline patrolling) and the whole system becomes easily scalable because each UAV can fly independently and the Ground Control Station does not represent a bottleneck. To deal with this task, a database consisting of more than 115.000 images was created to train and test several CNN architectures. Furthermore, an adaptive detection tracking system was introduced to make the whole system faster by optimizing the balance between new detections and tracking existing targets. The proposed solution is based on the measure of the tracking confidence and the frame similarity, by means of the Structural SIMilarity (SSIM) index, computed both globally and locally. Finally, the developed algorithms were tested on a realistic scenario by means of a UAV test-bed.

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Section: Detector-tracking Integrationmentioning

confidence: 99%

Adaptive Detection Tracking System for Autonomous UAV Maritime Patrolling

Panico

Fragonara

Al‐Rubaye

2020

2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace)

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“…When tracking a fast-moving target, the processing speed of the algorithm is particularly important. To improve real-time performance, a concept of ROI is proposed in [9] which means that detection is only executed within the region of interest. For instance, [10] use Calman filter to predict the next region of detection, [11] adopts Pyramid LK optical flow method and [12] adds Meanshift algorithm to TLD to get the region of interest.…”

Section: B Existing Defects and Possible Solutionsmentioning

confidence: 99%

An Improved TLD Tracking Algorithm for Fast-moving Object

Zhou¹,

Peng²,

Gong³

et al. 2018

Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

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Traditional object tracking is easily affected by deformation, scale changes, illumination changes, partial occlusions and so on. TLD(Tracking-Learning-Detection) is a classic effective algorithm in long-term tracking which can solve these problems well. Meanwhile, the real-time performance of the system should be taken into account while in the actual situation. An improved fast-moving object tracking algorithm based on TLD is proposed in this paper. In the paper, a method of narrowing the region of detection is proposed to effectively minimize the consumption of time, the method is combined with self-prediction of motion direction to ensure the accuracy of detection. To compensate for the possible missing and false detections caused by the reduction of detection region and the changing background, the variance threshold is updated dynamically to let more possible correct bounding boxes pass the variance classifier. Experiments have been conducted to verify the improved TLD algorithm, the results show that our algorithm ensures the accuracy of object tracking and has a good performance on the real-time.

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