Select Language

AI社区

公开数据集

跟踪多个地物段的视频分割数据集

跟踪多个地物段的视频分割数据集

199.52M
556 浏览
1 喜欢
0 次下载
0 条讨论
Deep Learning Classification

Segment Pool Tracking is the framework we presented for the video segmentation problem. The figure above illustrates the......

数据结构 ? 199.52M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Segment Pool Tracking is the framework we presented for the video segmentation problem. The figure above illustrates the core parts of our approach. First, we generate a pool of segmentation for each frame using the CPMC method. Then, image color features (eg. Color-SIFT) are extracted and appearance models are trained incrementally to track multiple segments in consecutive frames. A main contribution is an efficient least-squares formulation to make simultaneously tracking 1,000 targets almost as efficient as tracking a single target. Since the appearance model for each target is learnt over multiple frames on many segments, it is robust to appearance changes and partial occlusions. Lastly, we use Composite Statistical Inference (CSI) to refine segment tracks by infering on high-order appearance terms while imposing temporal consistency.


    During tracking, greedy assignment is applied that serves as non-maximum suppression on segment tracks (see figure below). Tracks that are not consistent in appearance are filtered out automatically. Therefore, although we initialize with more than 1,000 tracks, on average only 60 tracks remain at the end of each sequence, while capturing most of the interesting objects.




    Results of SPT and CSI on the SegTrack v2 dataset

    Image sequenceSPTSPT+CSIPairwise AppearanceLee et al.Grundmann et al.CPMC Best



    (Using Pirsiavash et al.)

    (Averaged Per-frame)
    Mean per object62.765.955.445.351.878.6
    Mean per sequence68.071.258.657.350.880.5
    Girl89.189.283.487.731.993.5
    Birdfall62.062.547.849.057.472.2
    Parachute93.293.491.396.369.195.5
    Cheetah-Deer40.137.318.344.518.867.0
    Cheetah-Cheetah41.340.922.211.724.466.6
    Monkeydog-Monkey58.871.324.174.368.383.0
    Monkeydog-Dog17.418.916.54.918.844.6
    Penguin-#151.451.559.312.672.075.8
    Penguin-#273.276.579.111.380.790.4
    Penguin-#369.675.275.611.375.285.4
    Penguin-#457.657.847.17.780.667.6
    Penguin-#563.466.745.84.262.768.1
    Penguin-#648.650.256.78.575.576.6
    Drifting Car-#173.874.865.463.755.282.1
    Drifting Car-#258.460.659.830.127.275.3
    Hummingbird-#145.454.435.046.313.770.0
    Hummingbird-#265.272.365.874.025.282.2
    Frog65.872.869.0067.187.1
    Worm75.682.859.584.434.789.8
    Soldier83.083.850.766.666.584.3
    Monkey84.184.870.979.061.988.3
    Bird of Paradise88.294.081.192.286.894.7
    BMX-Person75.185.474.587.439.286.9
    BMX-Bike24.624.930.938.632.558.5
    Avg. Number of Tracks60.060.0702.810.6336.61219.3

    Citation

    @inproceedings{FliICCV2013,
    author = {Fuxin Li and Taeyoung Kim and Ahmad Humayun and David Tsai and James M. Rehg},
    title = { Video Segmentation by Tracking Many Figure-Ground Segments},  
    booktitle = {ICCV},
    year = {2013} }




    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:11 去赚积分?
    • 556浏览
    • 0下载
    • 1点赞
    • 收藏
    • 分享