Class BYTETracker

tutorials/trades/byte_tracker.py:147–311

class BYTETracker(object):
    def __init__(self, args, frame_rate=30):
        self.args = args
        self.det_thresh = args.new_thresh
        self.buffer_size = int(frame_rate / 30.0 * args.track_buffer)
        self.max_time_lost = self.buffer_size
        self.reset()
    
    # below has no effect to final output, just to be compatible to codebase
    def init_track(self, results):
        for item in results:
            if item['score'] > self.opt.new_thresh and item['class'] == 1:
                self.id_count += 1
                item['active'] = 1
                item['age'] = 1
                item['tracking_id'] = self.id_count
                if not ('ct' in item):
                    bbox = item['bbox']
                    item['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
                self.tracks.append(item)
    
    def reset(self):
        self.frame_id = 0
        self.kalman_filter = KalmanFilter()
        self.tracked_stracks = []  # type: list[STrack]
        self.lost_stracks = []  # type: list[STrack]
        self.removed_stracks = []  # type: list[STrack]
        self.tracks = []    
        
        # below has no effect to final output, just to be compatible to codebase               
        self.id_count = 0
        
    def step(self, results, public_det=None):
        self.frame_id += 1
        activated_starcks = []
        refind_stracks = []
        lost_stracks = []
        removed_stracks = []
        detections = []
        detections_second = []
        
        scores = np.array([item['score'] for item in results if item['class'] == 1], np.float32)
        bboxes = np.vstack([item['bbox'] for item in results if item['class'] == 1])  # N x 4, x1y1x2y2
        
        remain_inds = scores >= self.args.track_thresh
        dets = bboxes[remain_inds]
        scores_keep = scores[remain_inds]
        
        
        inds_low = scores > self.args.out_thresh
        inds_high = scores < self.args.track_thresh
        inds_second = np.logical_and(inds_low, inds_high)
        dets_second = bboxes[inds_second]
        scores_second = scores[inds_second]        
        
        if len(dets) > 0:
            '''Detections'''
            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for