A target tracking method carried out by a computer, comprising: receiving a current frame of picture, wherein the current frame of picture comprises a target object; determining, based on a drift determining model, whether a tracker drifts for tracking of the target object in the current frame of picture, wherein the drift determining model is a probability density model and obtained through modeling based on largest values of response values of a sample, wherein the sample is a training sample used to train the drift determining model and collected from a training picture that comprises the target object, wherein the response value of the sample is a value indicating a probability that the training sample is the target object in the training picture; outputting (S 105) a tracking drift result, wherein the tracking drift result comprises: drift is generated for the tracking of the target object, or no drift is generated for the tracking of the target object; the method characterized in that, when the tracking drift result is that drift is generated for the tracking of the target object, the method further comprises: obtaining a corresponding position in the current frame of picture based on a position of the target object in a previous frame of picture, and establishing a plurality of search windows near the corresponding position; collecting samples in each of the plurality of search windows to obtain a sample set corresponding to each search window, and computing a sample response value of each sample in the sample set; determining, based on top k sample response values in each search window, a target search window in which the target object is most likely to appear, wherein the top k sample response values are k sample response values that are ranked top in descending order of sample response values; and determining a sampling sample corresponding to a largest sample response value in the target search window as the target object in the current frame of picture. receiving a current frame of picture, wherein the current frame of picture comprises a target object; determining, based on a drift determining model, whether a tracker drifts for tracking of the target object in the current frame of picture, wherein the drift determining model is a probability density model and obtained through modeling based on largest values of response values of a sample, wherein the sample is a training sample used to train the drift determining model and collected from a training picture that comprises the target object, wherein the response value of the sample is a value indicating a probability that the training sample is the target object in the training picture; outputting (S 105) a tracking drift result, wherein the tracking drift result comprises: drift is generated for the tracking of the target object, or no drift is generated for the tracking of the target object; the method characterized in that, when the tracking drift result is that drift is generated for the tracking of the target object, the method further comprises: obtaining a corresponding position in the current frame of picture based on a position of the target object in a previous frame of picture, and establishing a plurality of search windows near the corresponding position; collecting samples in each of the plurality of search windows to obtain a sample set corresponding to each search window, and computing a sample response value of each sample in the sample set; determining, based on top k sample response values in each search window, a target search window in which the target object is most likely to appear, wherein the top k sample response values are k sample response values that are ranked top in descending order of sample response values; and determining a sampling sample corresponding to a largest sample response value in the target search window as the target object in the current frame of picture. obtaining a corresponding position in the current frame of picture based on a position of the target object in a previous frame of picture, and establishing a plurality of search windows near the corresponding position; collecting samples in each of the plurality of search windows to obtain a sample set corresponding to each search window, and computing a sample response value of each sample in the sample set; determining, based on top k sample response values in each search window, a target search window in which the target object is most likely to appear, wherein the top k sample response values are k sample response values that are ranked top in descending order of sample response values; and determining a sampling sample corresponding to a largest sample response value in the target search window as the target object in the current frame of picture.