--- a
+++ b/mmaction/localization/proposal_utils.py
@@ -0,0 +1,95 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+
+
+def temporal_iou(proposal_min, proposal_max, gt_min, gt_max):
+    """Compute IoU score between a groundtruth bbox and the proposals.
+
+    Args:
+        proposal_min (list[float]): List of temporal anchor min.
+        proposal_max (list[float]): List of temporal anchor max.
+        gt_min (float): Groundtruth temporal box min.
+        gt_max (float): Groundtruth temporal box max.
+
+    Returns:
+        list[float]: List of iou scores.
+    """
+    len_anchors = proposal_max - proposal_min
+    int_tmin = np.maximum(proposal_min, gt_min)
+    int_tmax = np.minimum(proposal_max, gt_max)
+    inter_len = np.maximum(int_tmax - int_tmin, 0.)
+    union_len = len_anchors - inter_len + gt_max - gt_min
+    jaccard = np.divide(inter_len, union_len)
+    return jaccard
+
+
+def temporal_iop(proposal_min, proposal_max, gt_min, gt_max):
+    """Compute IoP score between a groundtruth bbox and the proposals.
+
+    Compute the IoP which is defined as the overlap ratio with
+    groundtruth proportional to the duration of this proposal.
+
+    Args:
+        proposal_min (list[float]): List of temporal anchor min.
+        proposal_max (list[float]): List of temporal anchor max.
+        gt_min (float): Groundtruth temporal box min.
+        gt_max (float): Groundtruth temporal box max.
+
+    Returns:
+        list[float]: List of intersection over anchor scores.
+    """
+    len_anchors = np.array(proposal_max - proposal_min)
+    int_tmin = np.maximum(proposal_min, gt_min)
+    int_tmax = np.minimum(proposal_max, gt_max)
+    inter_len = np.maximum(int_tmax - int_tmin, 0.)
+    scores = np.divide(inter_len, len_anchors)
+    return scores
+
+
+def soft_nms(proposals, alpha, low_threshold, high_threshold, top_k):
+    """Soft NMS for temporal proposals.
+
+    Args:
+        proposals (np.ndarray): Proposals generated by network.
+        alpha (float): Alpha value of Gaussian decaying function.
+        low_threshold (float): Low threshold for soft nms.
+        high_threshold (float): High threshold for soft nms.
+        top_k (int): Top k values to be considered.
+
+    Returns:
+        np.ndarray: The updated proposals.
+    """
+    proposals = proposals[proposals[:, -1].argsort()[::-1]]
+    tstart = list(proposals[:, 0])
+    tend = list(proposals[:, 1])
+    tscore = list(proposals[:, -1])
+    rstart = []
+    rend = []
+    rscore = []
+
+    while len(tscore) > 0 and len(rscore) <= top_k:
+        max_index = np.argmax(tscore)
+        max_width = tend[max_index] - tstart[max_index]
+        iou_list = temporal_iou(tstart[max_index], tend[max_index],
+                                np.array(tstart), np.array(tend))
+        iou_exp_list = np.exp(-np.square(iou_list) / alpha)
+
+        for idx, _ in enumerate(tscore):
+            if idx != max_index:
+                current_iou = iou_list[idx]
+                if current_iou > low_threshold + (high_threshold -
+                                                  low_threshold) * max_width:
+                    tscore[idx] = tscore[idx] * iou_exp_list[idx]
+
+        rstart.append(tstart[max_index])
+        rend.append(tend[max_index])
+        rscore.append(tscore[max_index])
+        tstart.pop(max_index)
+        tend.pop(max_index)
+        tscore.pop(max_index)
+
+    rstart = np.array(rstart).reshape(-1, 1)
+    rend = np.array(rend).reshape(-1, 1)
+    rscore = np.array(rscore).reshape(-1, 1)
+    new_proposals = np.concatenate((rstart, rend, rscore), axis=1)
+    return new_proposals