Methods, systems, apparatus and articles of manufacture to identify features within an image are disclosed herein. An example apparatus includes a horizontal cost (HCOST) engine to apply a first row of pixels of a macroblock to an input of a first HCOST unit, the first HCOST unit including a number of difference calculators; and a difference calculator engine to apply corresponding rows of pixels of a search window of a source image to corresponding ones of the number of difference calculators of the first HCOST unit, the corresponding ones of the number of difference calculators to calculate respective sums of absolute difference (SAD) values between (a) the first row of pixels of the macroblock and (b) the corresponding rows of pixels of the search window.

This application discloses an object detection method, a security device, and a readable storage medium. The object detection method includes: obtaining a plurality of first frames acquired by imaging an object within a first period of time; obtaining a plurality of second frames acquired by imaging the object within a second period of time, where an end time of the first period of time is a start time of the second period of time; comparing each of the second frames with the plurality of first frames one by one to determine whether a position variation of the object between the second frame and the first frame is less than a first preset threshold; if it is determined that the position variation of the object is less than the first preset threshold, increasing a count by one (1) and obtaining a total count after comparison with the plurality of second frames is completed; determining whether the total count reaches a second preset threshold; and if the total count reaches the second preset threshold, determining that the object is stationary; or if the total count does not reach the second preset threshold, determining that the object is moving. This application improves accuracy of a detection method such as an AI detection method in detecting a state of a stationary object and reduces false alarms.

A person tracking method, comprising: acquiring N frames in units of time windows; acquiring, in time windows, tracking paths of a target person according to the N frames; and constructing continuous tracking paths by means of continuous time windows, so as to obtain the tracking results of the target person.

A person tracking method, comprising: acquiring N frames in units of time windows; acquiring, in time windows, tracking paths of a target person according to the N frames; and constructing continuous tracking paths by means of continuous time windows, so as to obtain the tracking results of the target person.

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.

For each frame of a video, a determination is made whether an image of a hand exists in the frame. When at least one frame of the video includes the image of the hand, locations of the hand in the frames of the video are tracked to obtain a tracking result. A verification is performed to determine whether the tracking result is valid in a current frame of the frames of the video. When the tracking result is valid in the current frame of the video, a location of the hand is tracked in a next frame. When the tracking result is not valid in the current frame, localized hand image detection is performed on the current frame.

For each frame of a video, a determination is made whether an image of a hand exists in the frame. When at least one frame of the video includes the image of the hand, locations of the hand in the frames of the video are tracked to obtain a tracking result. A verification is performed to determine whether the tracking result is valid in a current frame of the frames of the video. When the tracking result is valid in the current frame of the video, a location of the hand is tracked in a next frame. When the tracking result is not valid in the current frame, localized hand image detection is performed on the current frame.

For each frame of a video, a determination is made whether an image of a hand exists in the frame. When at least one frame of the video includes the image of the hand, locations of the hand in the frames of the video are tracked to obtain a tracking result. A verification is performed to determine whether the tracking result is valid in a current frame of the frames of the video. When the tracking result is valid in the current frame of the video, a location of the hand is tracked in a next frame. When the tracking result is not valid in the current frame, localized hand image detection is performed on the current frame.