IEEE - Trans. ITS

[
    "Keywords" => [ "Vehicle part" , "Vehicle parts"],
    "Year_Filter" => [
            "2012-2016" => 14, 
            "2015-2016" => 6
        ]
    ],
    [
    "Keywords" => [ "Vehicle detection"],
    "Year_Filter" => [
            "2015-2017" => 8, 
        ]
]

1. [2014] Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance pdf

[
    "Main" => "Rear Detection with Mulitparts", 
    "Features" => 
        [
            "target" => "license plate+rear lamps",
            "detection_method" => "color conversion" 
        ],
    "Vehicle Detection" => "MRF to localize"    
    "Tracking" => "Kalman Filter"
]

PROS :



The relationships between the part nodes are represented by the Gaussian mixture model (GMM)

EXPERIMENT

[ "DB descript", "Accuracy", "Time" ]

2. [2013] Vehicle Detection by Independent Parts for Urban Driver Assistance pdf

Too Old

3. [2016] Multipart Vehicle Detection Using Symmetry-Derived Analysis and Active Learning pdf

multipart-based vehicle detection algorithm that employs Haar-like features and Adaboost classifiers for the detection of fully and partially visible rear views of vehicles.

4. [2016] Probabilistic Inference for Occluded and Multiview On-road Vehicle Detection pdf



Journal Construction

Selling point

  • ( Wheel part based + Spatial Relation Model )
  • Overtaking Vehicles
  1. Abstract

  2. Introduction

    • ITS => avoid accident
      • vehicle accidnet => [ "對撞","側撞","同向擦撞","追撞" ]
    • According to statistics from TW Gov., side crash 34% (?)
    • "",
    • Occlusion
      • => unreasonable to treat the vehicle as a whole
        • => a vehicle is composed of mulitparts
    • Wheels => stable vehcile part which can represent vehicles which earliest appear in the view
    • Wheel part based Vehicle Detection

  3. Related work 

    
    

  4. [Offline] ( HOG+MB-LBP ) + Adaboost

    • 3.1 Vehicle Decomposed + 3 aspect ratio of wheels (落點分佈圖)
    • 3.2 Detectors training process
    • 3.3 Spatial Relation Model

  5. [Online] Vehicle Detection

    • 4.1 (ROI + Wheel Filtering) => Wheel Detection
    • 4.2 Lane Detection + Localization

  6. Experimental Result

    • 5.1 Data set table 

    • 5.2 Performance table (Vehicle) 

    • 5.3 Performance table (Motor)

    • 5.4 Performance table (Bike)

    • 5.5 Time Consuming (???)

  7. Conclusion

  8. Reference

material

1.A1類：造成人員當場或二十四小時內死亡之交通事故。 2.A2類：造成人員受傷或超過二十四小時死亡之交通事故。 3.A3類：僅有財物損失之交通事故。

車與車-對撞 車與車-對向擦撞 車與車-同向擦撞 車與車-追撞 車與車-倒車撞 車與車-路口交岔撞 車與車-側撞 車與車-其他
102 3361 9243 34073 25857 2232 30713 93646 34645
103 3433 9682 36521 28730 2476 33381 102038 43367
104 3288 9583 34811 30509 2341 32147 100246 45474
Total 10082 28508 105405 85096 7049 96241 295930 123486
Percentage 1.3410535% 3.79198108% 14.0204071% 11.319013% 0.93762013% 12.801461% 39.3630195% 16.4254446%
751797

http://www.digitimes.com.tw/seminar/III_20161125/

而台灣與東南亞國家倚重2輪車為交通工具，根據台灣2013~2015交通事故統計資料，側撞(Side Crash)事故比例佔所有交通事故中的34.3%，側撞的車輛類型又以機車佔21.6%最高，小型車11.3%次之，而側撞事故發生地點絕大多數發生在十字路口上；有鑑於此，在台灣經濟部技術處支持下、財團法人資訊工業策進會智慧網通系統研究所(資策會智通所) 於今年六月與日本豐田IT開發中心合作，在「車聯網」及「自動駕駛」等相關議題上進行合作開發，希望解決社會大眾對於交通安全需求的車聯網應用服務。

Two-wheelers are one of the main transportation in Taiwan and Southeast Asian countries. According to Taiwan's 2013-2015 traffic accident statistics, side crash accounted for 34.3% of all traffic accidents. Side crash vehicle types: the motorcycle is the highest, accounting for 21.6%; the small car is the second, accounting for 11.3%. Side crash occurred mostly in the intersections, and we hope to improve traffic safety by this activity.

統計我國 103 年路口機車事故，發現其中有 46.7％是直行車輛 與其他左右轉汽機車碰撞造成的，此類肇事型態稱為側撞。以 103 年 為例，發生在路口的機車側撞車禍約 62,179 件，奪走 233 條機車族 生命，並造成 82,024 人

一般車輛肇事可分為對撞型態（碰撞角度介於180度與135度之間）事故、角撞型態（碰撞角度介於135度與45度之間）事故、側撞型態（碰撞角度介於45度與0度之間）事故及追撞型態（碰撞角度幾近於0度）事故[3]。

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