Machine learning with artificial intelligence algorithms

TRACKING ALGORITHM CLASSIFICATION The tracking algorithms can be classified by different criteria. In based on the techniques used for tracking, the author divides the trackers into two categories: the model-based and feature-based approaches. While a model-based approach needs the model for each tracked object (e.g. color model or contour model), the second approach uses visual features such as Histogram of Oriented Gradients (HOG) features to track the detected objects. In the tracking algorithms are classified into three approaches: appearance model-based, geometry model-based and probability-based approaches. The authors in divide the people tracking algorithms into two approaches: using human body parts and without using human body parts. we present the object tracking classification proposed by because this classification represents clearly and quite completely the tracking methods existing in the state of the art. This taxonomy method relies on the “tracked targets”. The tracked targets can be points of interest, appearance or silhouette of mobile object. Corresponding to these target types, three approach categories for object tracking are determined: point tracking, appearance tracking and silhouette the taxonomy of tracking methods

simultaneously using for Machine learning with artificial intelligence algorithms

There is several tracking algorithms available in CV libs like OpenCV. The most popular one in era of deep learning is perhaps GOTURN

https://www.learnopencv.com/goturn-deep-learning-based-object-tracking/

http://www.votchallenge.net

The Visual Object Tracking Challenge will give you a good overview. The publications section will give you a reasonable reading list to start with, too, including how object trackers are evaluated. In particular, take a look at the latest results - machine learning approaches were doing really well in recent years.

Be aware, though, that VOT is short term, single target tracking - you are given a bounding box in the first frame and, last time I checked, the sequences were all single camera, continuous sequences. Even the shape of the bounding box is up for research, with standard rectangles becoming properly segmented objects. Multiple object tracking has slightly different challenges, and long term tracking can involve relocating the target through cut scenes (so the target can undergo sharp changes in appearance and location). You'll need to decide which variety of object tracking interests you most.

YOLO is just detector, not tracker

Hi Devin,

Object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion.The object detection and object classification are preceding steps for tracking an object in sequence of images. Object detection is performed to check existence of objects in video and to precisely locate that object.

Refer the following Links: It may be useful

https://www.ripublication.com/ijcir17/ijcirv13n5_07.pdf

https://pdfs.semanticscholar.org/25a6/c5dff9a7019475daa81cd5a7f1f2dcdb5cf1.pdf

https://pdfs.semanticscholar.org/6b4c/2fd4327d4ad8f3f341c20f751cd4e4845a68.pdf

Dear Devin Delaney

I think the following review paper is the one of the completed one. it classified the state-of-the-art methods based on several parameters such as adaptivity and robustness:

Yang, H., Shao, L., Zheng, F., Wang, L. and Song, Z., 2011. Recent advances and trends in visual tracking: A review. Neurocomputing, 74(18), pp.3823-3831.

Moreover, the following recent review paper is sufficient for classifying the tracking method in recent decade:

Li, P., Wang, D., Wang, L. and Lu, H., 2018. Deep visual tracking: Review and experimental comparison. Pattern Recognition, 76, pp.323-338.

I hope it can be useful.

Regards