A hierarchical feature model for multi-target tracking

Ullah, Mohib; Mohammed, Ahmed; Cheikh, Faouzi Alaya; Wang, Zhaohui

doi:10.1109/icip.2017.8296755

Cited by 28 publications

(21 citation statements)

References 14 publications

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“…Feature extraction can usually be categorized based on the design approach. Feature engineering or handcrafted methods refer to a group of features designed by a human specialist, whereas deep features refer to a group of features extracted from deep artificial neural networks after proper training [42].…”

Section: Related Workmentioning

confidence: 99%

Class-Aware Image Search for Interpretable Cancer Identification

et al. 2020

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In recent times, the performance of computer-aided diagnosis systems in classification of malignancies has significantly improved. Search and retrieval methods are specifically important as they assist physicians in making the right diagnosis in medical imaging owing to their ability of obtaining similar cases for a query image. Supervised classification algorithms are generally more accurate than unsupervised search-based classifications; however, the latter may more easily provide insights into the decision-making process by providing a group of similar cases and their corresponding metadata (i.e., diagnostic reports) and not simply a class probability. In this study, we propose a class-aware search operating on deep image embeddings to increase the accuracy of content-based search. We validate our methodology using two different publicly available datasets, one containing endometrial cancer images and the other containing colorectal cancer images. The proposed class-aware scenarios can enhance the accuracy of the searchbased classifier, thereby making them more feasible in practice. With search results providing access to the metadata of retrieved cases (i.e., pathology reports of evidently diagnosed cases), such a combination has clear benefits for assisting experts with explainable results.

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Section: Related Workmentioning

confidence: 99%

Class-Aware Image Search for Interpretable Cancer Identification

et al. 2020

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“…After trajectories obtained using the above process, some of the trajectories will belong to the foreground while other trajectories will belong to background and noise [47][48] [49] [61]. In order to make the process efficient and effective, we remove trajectories belong to the background and noisy.…”

Section: Particle Advectionmentioning

confidence: 99%

Identifying Human Behavious Using Deep Trajectory Descriptors

Taghizadehghalehjoughi¹,

Alreshidi²

2019

Preprint

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Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We then compute trajectory descriptor for each segment which capture the structural relationship among different trajectories in the video segment. For trajectory descriptor, we project all extracted trajectories on the canvas. This will result in texture image which can store the relative motion and structural relationship among the trajectories. We then train Convolution Neural Network (CNN) to capture and learn the representation from dense trajectories. . Experimental results shows that our proposed method out performs state of the art methods by 90.01% on benchmark data set.

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“…The measurements not directly linked to an object's position are often combined into the object's appearance model. Kuo, Huang, and Nevatia (2010; color and histogram), Ullah, Mohammed, Cheikh, and Wang (2017; a CNN classifier), and Yinghui and Jianjun (2009; color histogram and image region covariance) all present different ways of incorporating an object's appearance model in the tracking and data association process. However, a general framework in which different appearance models can be included seems to be lacking.…”

Section: Introductionmentioning

confidence: 99%

Object detection, recognition, and tracking from UAVs using a thermal camera

Leira

Helgesen

Johansen

et al. 2020

Journal of Field Robotics

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In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.

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A hierarchical feature model for multi-target tracking

Cited by 28 publications

References 14 publications

Class-Aware Image Search for Interpretable Cancer Identification

Class-Aware Image Search for Interpretable Cancer Identification

Identifying Human Behavious Using Deep Trajectory Descriptors

Object detection, recognition, and tracking from UAVs using a thermal camera

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