Tracker assisted image capture

#The present disclosure relates generally to electronic devices. More specifically, the present disclosure relates to systems and methods for tracker assisted image capture.

#In the last several decades, the use of electronic devices has become common. In particular, advances in electronic technology have reduced the cost of increasingly complex and useful electronic devices. Cost reduction and consumer demand have proliferated the use of electronic devices such that they are practically ubiquitous in modern society. As the use of electronic devices has expanded, so has the demand for new and improved features of electronic devices. More specifically, electronic devices that perform new functions and/or that perform functions faster, more efficiently or with higher quality are often sought after.

#Some electronic devices (e.g., cameras, video camcorders, digital cameras, cellular phones, smart phones, computers, televisions, etc.) capture or utilize images. For example, a digital camera may capture a digital image.

#FIG. 1 is a block diagram illustrating an electronic device for use in the present systems and methods;

#FIG. 2A is a block diagram illustrating an object tracking and detection module;

#FIG. 2B illustrates some components within the system of FIG. 2A being implemented by a processor;

#Tracking an object within an image or a user-defined region of interest within that image using a camera from a mobile platform (e.g., tablets, phones) may be difficult. Real-time performance (˜30 frames per second (fps)) may be required. Some configurations may combine the output of an optical flow-based tracker and an image content-based detector to obtain robust tracking. However, the computation of the existing algorithms may be prohibitive for mobile platforms to achieve real-time performance.

#The present systems and methods may implement the following techniques to improve the speed of the tracking and detection algorithms: (1) using a fraction of possible detection windows at each frame, (e.g. randomly select the window positions); (2) selecting only a few spatial scales for object detection that are close to previous detected target size; (3) based on the confidence value of previous tracking, determining either to search the object in partial or the entire image; (4) dynamically adjusting the number of the detection windows based on previous tracking results; (5) instead of running the tracker and object detector in parallel, applying the tracker first, since it is less computationally expensive; and (6) running an object detector only when the confidence of the tracker is lower than a certain threshold. One of the technical advantages is to reduce computations used to track and/or detect a target object.

#One particular use of tracking and detection algorithms is picture processing. Picture processing may include taking a photograph and/or video editing. Implementing picture processing may provide real-world use applications of the tracking and detection algorithms described.