BoostTrack

Paper: BoostTrack++: Using Tracklet Information to Detect More Objects in Multiple Object Tracking

BoostTrack++ focuses on a neglected part of MOT pipelines: deciding which detections are worth trusting in the first place. The paper extends BoostTrack by using tracklet history to build a richer similarity score, then boosts low-confidence detections when past evidence suggests they are real objects. In practice, that improves recall and identity stability without giving up the online tracking-by-detection setup.

What BoxMOT Needs For BoostTrack

• A detector and, by default, a ReID model for the full configuration.
• AABB detections only in BoxMOT.
• Best when low-confidence true positives are a recurring problem and you want stronger association scoring than plain IoU or Mahalanobis distance.

Tuning notes

Adaptive Kalman Filter (adaptive_kf)

When enabled, the process noise covariance Q is estimated online from innovation statistics (Mehra 1970) rather than kept constant. A sliding window (30 frames, warmup 15) accumulates the Kalman innovations, and once warmed up the estimated Q is blended (α = 0.7) with the default static Q.

When to use it:

• Deploying to a new domain where you have no ground truth to run --tune-kf.
• Scenes where camera motion compensation (CMC) may fail intermittently (low-texture, rain, night).
• Camera dynamics that vary significantly within a single sequence (e.g., drone footage alternating hover and fast sweep).

When NOT to use it:

• You already have a tuned static Q from boxmot eval --tune-kf on representative data — the static solution is cheaper and deterministic.
• Very short tracks (< 15 frames) dominate; the estimator never exits warmup so it adds overhead with no benefit.

Enable it from the CLI:

boxmot track --tracker boosttrack --adaptive-kf
boxmot eval  --tracker boosttrack --adaptive-kf

Or in the tracker config YAML:

adaptive_kf: true

Bases: BaseTracker

Initialize the BoostTrack tracker.

Parameters:

Name	Type	Description	Default
reid_model	Any | None	Pre-built ReID backend model (e.g. ReID(...).model).	None
use_ecc	bool	Whether to enable camera-motion compensation.	True
min_box_area	int	Minimum detection area.	10
aspect_ratio_thresh	float	Maximum accepted aspect ratio.	1.6
cmc_method	str	Camera-motion compensation method.	'ecc'
lambda_iou	float	Weight applied to IoU association.	0.5
lambda_mhd	float	Weight applied to Mahalanobis association.	0.25
lambda_shape	float	Weight applied to shape similarity.	0.25
use_dlo_boost	bool	Whether to enable DLO boosting.	True
use_duo_boost	bool	Whether to enable DUO boosting.	True
dlo_boost_coef	float	Coefficient used by DLO boosting.	0.65
s_sim_corr	bool	Whether to enable shape-similarity correction.	False
use_rich_s	bool	Whether to enable rich shape features.	False
use_sb	bool	Whether to enable soft-BIoU.	False
use_vt	bool	Whether to enable visual tracking cues.	False
with_reid	bool	Whether to enable ReID features.	False
reid_model	Any | None	Pre-built ReID backend model (e.g. ReID(...).model).	None
**kwargs	Any	Base tracker settings forwarded to :class:BaseTracker.	{}

Attributes:

Name	Type	Description
frame_count	int	Number of processed frames.
active_tracks	list	Currently active tracks.
trackers	list[KalmanBoxTracker]	Internal Kalman trackers.
cmc		Camera-motion compensation method when enabled.
reid_model		ReID model used for appearance extraction when enabled.

Source code in boxmot/trackers/bbox/boosttrack/boosttrack.py

class BoostTrack(BaseTracker):
    """Initialize the BoostTrack tracker.

    Args:
        reid_model: Pre-built ReID backend model (e.g. ``ReID(...).model``).
        use_ecc (bool): Whether to enable camera-motion compensation.
        min_box_area (int): Minimum detection area.
        aspect_ratio_thresh (float): Maximum accepted aspect ratio.
        cmc_method (str): Camera-motion compensation method.
        lambda_iou (float): Weight applied to IoU association.
        lambda_mhd (float): Weight applied to Mahalanobis association.
        lambda_shape (float): Weight applied to shape similarity.
        use_dlo_boost (bool): Whether to enable DLO boosting.
        use_duo_boost (bool): Whether to enable DUO boosting.
        dlo_boost_coef (float): Coefficient used by DLO boosting.
        s_sim_corr (bool): Whether to enable shape-similarity correction.
        use_rich_s (bool): Whether to enable rich shape features.
        use_sb (bool): Whether to enable soft-BIoU.
        use_vt (bool): Whether to enable visual tracking cues.
        with_reid (bool): Whether to enable ReID features.
        reid_model: Pre-built ReID backend model (e.g. ``ReID(...).model``).
        **kwargs: Base tracker settings forwarded to :class:`BaseTracker`.

    Attributes:
        frame_count (int): Number of processed frames.
        active_tracks (list): Currently active tracks.
        trackers (list[KalmanBoxTracker]): Internal Kalman trackers.
        cmc: Camera-motion compensation method when enabled.
        reid_model: ReID model used for appearance extraction when enabled.
    """

    def __init__(
        self,
        reid_model: Any | None = None,
        # BoostTrack-specific parameters
        use_ecc: bool = True,
        min_box_area: int = 10,
        aspect_ratio_thresh: float = 1.6,
        cmc_method: str = "ecc",
        lambda_iou: float = 0.5,
        lambda_mhd: float = 0.25,
        lambda_shape: float = 0.25,
        use_dlo_boost: bool = True,
        use_duo_boost: bool = True,
        dlo_boost_coef: float = 0.65,
        s_sim_corr: bool = False,
        use_rich_s: bool = False,
        use_sb: bool = False,
        use_vt: bool = False,
        with_reid: bool = False,
        adaptive_kf: bool = False,
        **kwargs: Any,  # BaseTracker parameters
    ):
        # Capture all init params for logging
        init_args = {k: v for k, v in locals().items() if k not in ('self', 'kwargs')}
        super().__init__(**init_args, _tracker_name='BoostTrack', **kwargs)

        self.active_tracks = []
        self.frame_count = 0
        self.trackers: List[KalmanBoxTracker] = []

        # Parameters for BoostTrack (these can be tuned as needed)
        self.use_ecc = use_ecc            # use ECC for camera motion compensation
        self.min_box_area = min_box_area  # minimum box area for detections
        self.aspect_ratio_thresh = aspect_ratio_thresh  # aspect ratio threshold for detections
        self.cmc_method = cmc_method

        self.lambda_iou = lambda_iou
        self.lambda_mhd = lambda_mhd
        self.lambda_shape = lambda_shape
        self.use_dlo_boost = use_dlo_boost
        self.use_duo_boost = use_duo_boost
        self.dlo_boost_coef = dlo_boost_coef
        self.s_sim_corr = s_sim_corr

        self.use_rich_s = use_rich_s
        self.use_sb = use_sb
        self.use_vt = use_vt

        self.with_reid = with_reid and reid_model is not None
        self.reid_model = reid_model if self.with_reid else None
        self.adaptive_kf = bool(adaptive_kf)

        if self.use_ecc:
            self.cmc = get_cmc_method(cmc_method)()
        else:
            self.cmc = None

    def _update_impl(self, dets: np.ndarray, img: np.ndarray, embs: Optional[np.ndarray] = None, masks: np.ndarray = None) -> np.ndarray:
        """
        Update the tracker with detections and an image.

        Args:
          dets (np.ndarray): Detection boxes in the format [[x1,y1,x2,y2,score], ...]
          img (np.ndarray): The current image frame.
          embs (Optional[np.ndarray]): Optional precomputed embeddings.

        Returns:
          np.ndarray: Tracked objects in the format
                      [x1, y1, x2, y2, id, confidence, cls, det_ind]
                      (with cls and det_ind set to -1 if unused)
        """
        self.check_inputs(dets=dets, embs=embs, img=img)

        dets = np.hstack([dets, np.arange(len(dets)).reshape(-1, 1)])

        self.frame_count += 1

        if self.cmc is not None:
            transform = self.cmc.apply(img, dets)
            for trk in self.trackers:
                trk.camera_update(transform)

        trks = []
        confs = []

        for trk in self.trackers:
            pos = trk.predict()[0]
            conf = trk.get_confidence()
            confs.append(conf)
            trks.append(np.concatenate([pos, [conf]]))
        trks_np = np.vstack(trks) if len(trks) > 0 else np.empty((0, 5))

        if self.use_dlo_boost:
            dets = self.dlo_confidence_boost(dets)
        if self.use_duo_boost:
            dets = self.duo_confidence_boost(dets)

        dets_embs = np.ones((dets.shape[0], 1))
        if dets.size > 0:
            remain_inds = dets[:, 4] >= self.det_thresh
            dets = dets[remain_inds]
            scores = dets[:, 4]

            if self.with_reid:
                if embs is not None:
                    dets_embs = embs[remain_inds]
                else:
                    dets_embs = self.reid_model.get_features(dets[:, :4], img)
        else:
            scores = np.empty(0)
            dets_embs = np.ones((dets.shape[0], 1))

        if self.with_reid and len(self.trackers) > 0:
            tracker_embs = np.array([trk.get_emb() for trk in self.trackers])
            if dets_embs.shape[0] == 0:
                emb_cost = np.empty((0, tracker_embs.shape[0]))
            else:
                emb_cost = dets_embs.reshape(dets_embs.shape[0], -1) @ tracker_embs.reshape((tracker_embs.shape[0], -1)).T
        else:
            emb_cost = None

        mh_dist_matrix = self.get_mh_dist_matrix(dets)

        matched, unmatched_dets, unmatched_trks, _ = associate(
            dets,
            trks_np,
            self.iou_threshold,
            mahalanobis_distance=mh_dist_matrix,
            track_confidence=np.array(confs).reshape(-1, 1),
            detection_confidence=scores,
            emb_cost=emb_cost,
            lambda_iou=self.lambda_iou,
            lambda_mhd=self.lambda_mhd,
            lambda_shape=self.lambda_shape,
            s_sim_corr=self.s_sim_corr,
        )

        if dets.size > 0:
            trust = (dets[:, 4] - self.det_thresh) / (1 - self.det_thresh)
            af = 0.95
            dets_alpha = af + (1 - af) * (1 - trust)
        else:
            dets_alpha = np.empty(0)

        for m in matched:
            self.trackers[m[1]].update(dets[m[0], :])
            self.trackers[m[1]].update_emb(dets_embs[m[0]], alpha=dets_alpha[m[0]])

        for i in unmatched_dets:
            if dets[i, 4] >= self.det_thresh:
                self.trackers.append(
                    KalmanBoxTracker(dets[i, :], max_obs=self.max_obs, emb=dets_embs[i], adaptive_kf=self.adaptive_kf)
                )

        outputs = []
        self.active_tracks = []
        for trk in self.trackers:
            d = trk.get_state()[0]
            if (trk.time_since_update < 1) and (
                trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits
            ):
                # Format: [x1, y1, x2, y2, id, confidence, cls, det_ind]
                outputs.append(np.array([d[0], d[1], d[2], d[3], trk.id, trk.conf, trk.cls, trk.det_ind]))
                self.active_tracks.append(trk)

        self.trackers = [trk for trk in self.trackers if trk.time_since_update <= self.max_age]

        if len(outputs) == 0:
            return np.empty((0, 8))
        outputs = np.vstack(outputs)
        return self.filter_outputs(outputs)

    def filter_outputs(self, outputs: np.ndarray) -> np.ndarray:

        w_arr = outputs[:, 2] - outputs[:, 0]
        h_arr = outputs[:, 3] - outputs[:, 1]

        vertical_filter = w_arr / h_arr <= self.aspect_ratio_thresh
        area_filter = w_arr * h_arr > self.min_box_area

        return outputs[vertical_filter & area_filter]

    def get_iou_matrix(self, detections: np.ndarray, buffered: bool = False) -> np.ndarray:
        trackers = np.zeros((len(self.trackers), 5))
        for t, trk in enumerate(trackers):
            pos = self.trackers[t].get_state()[0]
            trk[:] = [pos[0], pos[1], pos[2], pos[3], self.trackers[t].get_confidence()]

        return iou_batch(detections, trackers) if not buffered else soft_biou_batch(detections, trackers)

    def get_mh_dist_matrix(self, detections: np.ndarray, n_dims: int = 4) -> np.ndarray:
        if len(self.trackers) == 0:
            return np.zeros((0, 0))
        z = np.zeros((len(detections), n_dims), dtype=float)
        x = np.zeros((len(self.trackers), n_dims), dtype=float)
        sigma_inv = np.zeros((len(self.trackers), n_dims), dtype=float)

        for i in range(len(detections)):
            z[i, :n_dims] = convert_bbox_to_z(detections[i, :]).reshape(-1)[:n_dims]
        for i, trk in enumerate(self.trackers):
            x[i] = trk.kf.x[:n_dims]
            sigma_inv[i] = np.reciprocal(np.diag(trk.kf.covariance[:n_dims, :n_dims]))
        return ((z.reshape((-1, 1, n_dims)) - x.reshape((1, -1, n_dims))) ** 2 *
                sigma_inv.reshape((1, -1, n_dims))).sum(axis=2)


    def duo_confidence_boost(self, detections: np.ndarray) -> np.ndarray:
        if len(detections) == 0:
            return detections

        n_dims = 4
        limit = 13.2767
        mh_dist = self.get_mh_dist_matrix(detections, n_dims)

        # If there are no existing trackers, bail out immediately
        if mh_dist.size == 0:
            return detections

        min_dists = mh_dist.min(1)
        mask = (min_dists > limit) & (detections[:, 4] < self.det_thresh)
        boost_inds = np.where(mask)[0]
        iou_limit = 0.3
        if len(boost_inds) == 0:
            return detections

        bdiou = iou_batch(detections[boost_inds], detections[boost_inds]) - np.eye(
            len(boost_inds)
        )
        bdiou_max = bdiou.max(axis=1)
        remaining = boost_inds[bdiou_max <= iou_limit]
        args = np.where(bdiou_max > iou_limit)[0]
        for i in range(len(args)):
            bi = args[i]
            tmp = np.where(bdiou[bi] > iou_limit)[0]
            args_tmp = np.append(
                np.intersect1d(boost_inds[args], boost_inds[tmp]), boost_inds[bi]
            )
            conf_max = np.max(detections[args_tmp, 4])
            if detections[boost_inds[bi], 4] == conf_max:
                remaining = np.concatenate([remaining, [boost_inds[bi]]])

        mask_boost = np.zeros_like(detections[:, 4], dtype=bool)
        mask_boost[remaining] = True
        detections[:, 4] = np.where(
            mask_boost, self.det_thresh + 1e-4, detections[:, 4]
        )
        return detections

    def dlo_confidence_boost(self, detections: np.ndarray) -> np.ndarray:
        if len(detections) == 0:
            return detections

        sbiou_matrix = self.get_iou_matrix(detections, True)
        if sbiou_matrix.size == 0:
            return detections

        trackers = np.zeros((len(self.trackers), 6))
        for t, trk in enumerate(self.trackers):
            pos = trk.get_state()[0]
            trackers[t] = [pos[0], pos[1], pos[2], pos[3], 0, trk.time_since_update - 1]

        if self.use_rich_s:
            mhd_sim = MhDist_similarity(self.get_mh_dist_matrix(detections), 1)
            shape_sim = shape_similarity(detections, trackers, self.s_sim_corr)
            S = (mhd_sim + shape_sim + sbiou_matrix) / 3
        else:
            S = self.get_iou_matrix(detections, False)

        if not self.use_sb and not self.use_vt:
            max_s = S.max(1)
            detections[:, 4] = np.maximum(detections[:, 4], max_s * self.dlo_boost_coef)
            return detections

        if self.use_sb:
            max_s = S.max(1)
            alpha = 0.65
            detections[:, 4] = np.maximum(
                detections[:, 4], alpha * detections[:, 4] + (1 - alpha) * max_s**1.5
            )
        if self.use_vt:
            threshold_s = 0.95
            threshold_e = 0.8
            tmp = (S > np.maximum(
                threshold_s - np.array([trk.time_since_update - 1 for trk in self.trackers]),
                                    threshold_e)).max(1)
            scores = detections[:, 4].copy()
            scores[tmp] = np.maximum(scores[tmp], self.det_thresh + 1e-5)
            detections[:, 4] = scores
        return detections