Method update

tutorials/jde/tracker.py:177–374

Processes the image frame and finds bounding box(detections). Associates the detection with corresponding tracklets and also handles lost, removed, refound and active tracklets Parameters ---------- im_blob : torch.float32 Tensor o

(self, im_blob, img0)

        self.kalman_filter = KalmanFilter()

    def update(self, im_blob, img0):
        """
        Processes the image frame and finds bounding box(detections).

        Associates the detection with corresponding tracklets and also handles lost, removed, refound and active tracklets

        Parameters
        ----------
        im_blob : torch.float32
                  Tensor of shape depending upon the size of image. By default, shape of this tensor is [1, 3, 608, 1088]

        img0 : ndarray
               ndarray of shape depending on the input image sequence. By default, shape is [608, 1080, 3]

        Returns
        -------
        output_stracks : list of Strack(instances)
                         The list contains information regarding the online_tracklets for the recieved image tensor.

        """

        self.frame_id += 1
        activated_starcks = []      # for storing active tracks, for the current frame
        refind_stracks = []         # Lost Tracks whose detections are obtained in the current frame
        lost_stracks = []           # The tracks which are not obtained in the current frame but are not removed.(Lost for some time lesser than the threshold for removing)
        removed_stracks = []

        t1 = time.time()
        ''' Step 1: Network forward, get detections & embeddings'''
        with torch.no_grad():
            pred = self.model(im_blob)
        # pred is tensor of all the proposals (default number of proposals: 54264). Proposals have information associated with the bounding box and embeddings
        pred = pred[pred[:, :, 4] > self.low_thresh]
        # pred now has lesser number of proposals. Proposals rejected on basis of object confidence score
        if len(pred) > 0:
            dets = non_max_suppression(pred.unsqueeze(0), self.low_thresh, self.opt.nms_thres)[0].cpu()
            # Final proposals are obtained in dets. Information of bounding box and embeddings also included
            # Next step changes the detection scales
            scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
            '''Detections is list of (x1, y1, x2, y2, object_conf, class_score, class_pred)'''
            # class_pred is the embeddings.

            dets = dets.numpy()
            remain_inds = dets[:, 4] > self.det_thresh
            inds_low = dets[:, 4] > self.low_thresh
            inds_high = dets[:, 4] < self.det_thresh
            inds_second = np.logical_and(inds_low, inds_high)
            dets_second = dets[inds_second]
            dets = dets[remain_inds]

            detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for
                          (tlbrs, f) in zip(dets[:, :5], dets[:, 6:])]
        else:
            detections = []
            dets_second = []

        t2 = time.time()
        # print('Forward: {} s'.format(t2-t1))