A method for tracking at least one object by using a convolutional neural network, CNN, including a tracking network, comprising steps of: (a) a testing device (100, 200), generating at least one feature map by using a current video frame of an inputted video, and instructing a region proposal network, RPN (122; 222), to apply at least one operation to the feature map, to thereby generate information on one or more proposal boxes corresponding to an object; (b) the testing device (100, 200) (i) (i-1) generating an estimated state vector of a previous bounding box, which has at least one possibility of including the object on the current video frame, from a previous state vector of the previous bounding box corresponding to the object positioned on a previous video frame by using a Kalman filter algorithm (123; 223), (i-2) generating an estimated bounding box, wherein the estimated bounding box corresponding to the estimated state vector includes information on an estimated position, on the current video frame, tracked from a location of the previous bounding box, and (i-3) determining a specific proposal box selected by referring to the estimated bounding box among the proposal boxes as a seed box, (c) the testing device instructing to thereby generate a regression delta and a class score corresponding to the seed box; and (d) the testing device generating a current bounding box corresponding to the object on the current video frame by referring to the regression delta and the seed box, and adjusting the current bounding box by using the Kalman filter algorithm (123; 223) to output an adjusted bounding box, wherein the step (c) of the testing device instructing to thereby generate a regression delta and a class score either comprises: c1) the test device instructing a fully convolutional network, FCN, to apply one or more full convolution operations to the feature map, to thereby output at least one position sensitive score map (PSSM) and instructing at least one pooling layer to pool a region, corresponding to the seed box, on the position sensitive score map (PSSM), to thereby generate the regression delta and the class score, or comprises c2) the testing device instructing at least one pooling layer to pool a region, corresponding to the seed box, on the feature map, to thereby generate a feature vector, and instructing at least one fully connected layer, FC, to apply at least one fully connected operation to the feature vector, to thereby generate the regression delta and the class score corresponding to the seed box. (a) a testing device (100, 200), generating at least one feature map by using a current video frame of an inputted video, and instructing a region proposal network, RPN (122; 222), to apply at least one operation to the feature map, to thereby generate information on one or more proposal boxes corresponding to an object; (b) the testing device (100, 200) (i) (i-1) generating an estimated state vector of a previous bounding box, which has at least one possibility of including the object on the current video frame, from a previous state vector of the previous bounding box corresponding to the object positioned on a previous video frame by using a Kalman filter algorithm (123; 223), (i-2) generating an estimated bounding box, wherein the estimated bounding box corresponding to the estimated state vector includes information on an estimated position, on the current video frame, tracked from a location of the previous bounding box, and (i-3) determining a specific proposal box selected by referring to the estimated bounding box among the proposal boxes as a seed box, (c) the testing device instructing to thereby generate a regression delta and a class score corresponding to the seed box; and (d) the testing device generating a current bounding box corresponding to the object on the current video frame by referring to the regression delta and the seed box, and adjusting the current bounding box by using the Kalman filter algorithm (123; 223) to output an adjusted bounding box, c1) the test device instructing a fully convolutional network, FCN, to apply one or more full convolution operations to the feature map, to thereby output at least one position sensitive score map (PSSM) and instructing at least one pooling layer to pool a region, corresponding to the seed box, on the position sensitive score map (PSSM), to thereby generate the regression delta and the class score, or comprises c2) the testing device instructing at least one pooling layer to pool a region, corresponding to the seed box, on the feature map, to thereby generate a feature vector, and instructing at least one fully connected layer, FC, to apply at least one fully connected operation to the feature vector, to thereby generate the regression delta and the class score corresponding to the seed box.