摘要：【中文】本申请涉及目标对象的位置区域确定方法、装置、电子设备及存储介质，该方法通过获取图像序列；获取目标对象在图像序列的当前帧图像中的当前位置区域，并基于当前位置区域确定搜索区域；从当前位置区域确定第一特征信息；第一特征信息包括当前位置区域的语义信息；从搜索区域确定第二特征信息；第二特征信息包括搜索区域的语义信息；基于第一特征信息和第二特征信息确定相似程度值集合；从相似程度值集合确定目标相似程度值；基于目标相似程度值和当前位置区域的尺寸确定目标对象在下一帧图像中的位置区域。如此，通过学习到更高级的语义信息，可以提高对目标对象位置区域跟踪确定的准确度，可以提高目标对象的跟踪精度和鲁棒性。 【EN】The application relates to a method, a device, an electronic device and a storage medium for determining a position area of a target object, wherein the method comprises the steps of acquiring an image sequence; acquiring a current position area of a target object in a current frame image of an image sequence, and determining a search area based on the current position area; determining first characteristic information from a current location area; the first characteristic information comprises semantic information of a current position area; determining second feature information from the search area; the second feature information includes semantic information of the search area; determining a set of similarity degree values based on the first feature information and the second feature information; determining a target similarity value from the similarity value set; and determining the position area of the target object in the next frame of image based on the target similarity value and the size of the current position area. In this way, by learning higher-level semantic information, the accuracy of tracking and determining the target object position region can be improved, and the tracking accuracy and robustness of the target object can be improved.

摘要：【中文】本申请实施例所公开的一种目标对象跟踪方法、系统、电子设备及存储介质，其中，方法包括根据样本图片中样本对象的第一位置区域和多个预设尺度值从对比图片中确定样本对象的多个搜索区域，从第一位置区域中确定样本对象的第一特征集合，从多个搜索区域中确定样本对象在每个搜索区域的第二特征集合，根据第一特征集合和多个第二特征集合确定多个匹配值集合，根据所述多个匹配值集合与预设匹配值的差值确定多个损失值以调整训练跟踪模型的参数，得到训练后的跟踪模型，基于训练后的跟踪模型，能够提高跟踪目标对象的准确性和鲁棒性。 【EN】The method comprises the steps of determining a plurality of search areas of a sample object from a comparison picture according to a first position area of the sample object in a sample picture and a plurality of preset scale values, determining a first feature set of the sample object from the first position area, determining a second feature set of the sample object in each search area from the plurality of search areas, determining a plurality of matching value sets according to the first feature set and the plurality of second feature sets, determining a plurality of loss values according to difference values of the plurality of matching value sets and the preset matching values to adjust parameters of a training tracking model, obtaining the trained tracking model, and improving the accuracy and robustness of the tracking target object based on the trained tracking model.