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MOTR: End-to-End Multiple-Object Tracking with Transformer

Posted on Edited on In Valine:

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TL;DR

  • 提出了一个完全端到端的多目标跟踪框架
  • 将多目标跟踪问题形式化为一组序列预测问题
  • 引入了跟踪感知的标签分配
  • 提出了用于时间建模的集体平均损失和时间聚合网络方法

Algorithm

MOTR 整体流程

MOTR.png

  1. 特征提取：用 CNN backbone 提取连续帧中每一帧的特征（上图中的 Enc）
  2. 查询生成：用 Deformable Transformer 对第一步提取的特征进行查询（上图中的 Dec）
    • 对于视频第一帧，只解码 object detection query （上图中的 qdq_d ）得到 hidden state
    • 对于非第一帧，将 object detection query （上图中的 qdq_d ）和上一帧的 tracking query （上图中的 qtrq_{tr} ）先 concat 再进行解码得到 hidden state
  3. 预测结果生成：用一个简单的结构将上一步得到的 hidden state 映射到任务空间，预测结果包含 object detection results 和 tracking results
  4. 得到下一帧的 tracking query：用 QIM (Query Interaction Module, 查询交互模块) 将上一步得到的预测结果映射为下一帧的 tracking query
  5. 计算损失 / 输出预测结果：对于训练，计算集体平均损失（CAL, Collective Average Loss）;对于预测，直接输出第 3 步得到的结果
  • 描述 MOTR 过程的伪代码
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def process_frame(frame, detect_queries, track_queries=None, ground_truths=None):
    # 使用CNN提取帧特征
    # frame shape: (height, width, channels)
    frame_features = extract_frame_features(frame)  # Shape: (height, width, channels)
    if track_queries is None:
        # 使用Deformable DETR解码器生成隐藏状态
        # detect_queries shape: (num_queries, query_dim)
        # frame_features shape: (height, width, channels)
        hidden_states = deformable_detr_decoder(detect_queries, frame_features)  # Shape: (num_queries, hidden_dim)
    else:
        queries = concatenate(track_queries, detect_queries)  # Shape: (num_queries + num_tracks, query_dim)
        hidden_states = deformable_detr_decoder(queries, frame_features)  # Shape: (num_queries + num_tracks, hidden_dim)
    # 生成预测
    # hidden_states shape: (num_queries, hidden_dim)
    predictions = predict(hidden_states)  # Shape: (num_queries + num_tracks, num_classes + 4)
    # 使用Query Interaction Module (QIM)生成下一帧的跟踪查询
    # hidden_states shape: (num_queries, hidden_dim)
    track_queries = qim(hidden_states)  # Shape: (num_tracks, query_dim)
    if ground_truths is not None:
        # 使用Collective Average Loss (CAL)进行训练
        # predictions shape: (num_queries, num_classes + 4)
        # ground_truths shape: (num_objects, num_classes + 4)
        loss = cal(predictions, ground_truths)
        backpropagate(loss)
    return predictions, track_queries  # Shape: (num_queries + num_tracks, num_classes + 4), (num_tracks, query_dim)
def process_video(video, ground_truths=None):
    # 初始化检测查询
    # 返回形状：(num_queries, query_dim)
    detect_queries = initialize_detect_queries()  
    track_queries = None  # Shape: (num_tracks, query_dim)
    for frame in video:
        predictions, track_queries = process_frame(frame, detect_queries, track_queries, ground_truths)
        if ground_truths is None:
            yield predictions

查询交互模块

  • 查询交互模块 Query Interaction Module (QIM) 是 MOTR 中的一个关键组件，它负责处理物体的进入和退出，以及增强长期的时间关系建模
  • QIM 的输入是当前帧预测的 detection result 和 tracking result，输出是下一帧的 tacking query
  • 通俗来说，QIM 是根据当前帧预测的结果，给出下一帧的 “提问”
  • QIM 过程的伪代码
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def query_interaction_module(hidden_states, scores, tau_en, tau_ex, M):
    # hidden_states shape: (num_queries, hidden_dim)
    # scores shape: (num_queries, num_classes)
    # tau_en, tau_ex: entrance and exit thresholds
    # M: number of consecutive frames for exit threshold
    # Object Entrance
    entrance_mask = scores.max(dim=1) > tau_en  # Shape: (num_queries,)
    hidden_states = hidden_states[entrance_mask]  # Shape: (num_entrance_queries, hidden_dim)
    # Temporal Aggregation Network (TAN)，主要目的是融合时序信息，本文是用了一个 Multi-Head Self-Attention 实现
    hidden_states = temporal_aggregation_network(hidden_states)  # Shape: (num_entrance_queries, hidden_dim)
    # Object Exit
    exit_mask = scores.max(dim=1) < tau_ex  # Shape: (num_entrance_queries,)
    exit_mask = exit_mask.rolling(window=M).sum() > 0  # Shape: (num_entrance_queries,)
    hidden_states = hidden_states[~exit_mask]  # Shape: (num_track_queries, hidden_dim)
    return hidden_states  # Shape: (num_track_queries, hidden_dim)

集体平均损失

  • 集体平均损失（Collective Average Loss，CAL）是 MOTR 算法中用于训练的损失函数。不同于传统的逐帧计算损失，CAL 收集整个视频剪辑的所有预测，然后基于整个视频剪辑计算总体损失
  • 集体平均损失的代码描述
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def collective_average_loss(predictions, ground_truths, matching_results):
    total_loss = 0
    total_objects = 0
    for i in range(len(predictions)):
        pred_tracked = predictions[i]['tracked']
        pred_detected = predictions[i]['detected']
        gt_tracked = ground_truths[i]['tracked']
        gt_detected = ground_truths[i]['detected']
        match_tracked = matching_results[i]['tracked']
        match_detected = matching_results[i]['detected']
        total_loss += single_frame_loss(pred_tracked, match_tracked, gt_tracked)
        total_loss += single_frame_loss(pred_detected, match_detected, gt_detected)
        total_objects += len(gt_tracked) + len(gt_detected)
    return total_loss / total_objects

Thought

  • 以一种非常优雅的方式解决了端到端多目标追踪的任务，打破了之前 NN detection + Hard logic code tracking 的 tracking 范式
  • 这种非黑盒的（显式监督 detecion bbox）复杂任务端到端训练，启发了后续的许多更复杂的端到端任务，例如 UniAD