Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors

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1 Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors September 2007 Fumio Yamazaki 1, Wen Liu 1, T. Thuy Vu 2 1. Graduate School of Engineering, Chiba University, Japan. 2. National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan. 1

2 Background Satellite remote sensing and aerial photography have been used to capture still (snapshot) images of the earth surface. But if two images with a small time interval are taken, they can be employed to detect moving objects, e.g. cars, and even measure their speeds. If such data are available, many new applications can be considered such as observing highway traffic conditions. 2

But if two images with a small time interval are taken, they can be employed to detect moving

3 Objectives Demonstrate the possibility and limitation of detecting moving objects from a QuickBird (QB) scene using a slight time lag (=0.2 seconds) between panchromatic (PAN) and multi-spectral (MS) sensors. Develop a new object-based method to extract moving vehicles and subsequently detect their speeds from two consecutive aerial images automatically. 3

2 seconds) between panchromatic (PAN) and multi-spectral (MS) sensors.

4 Contents Time lag in QB images and moving objects seen in Google Earth Visual inspection of vehicle speed from QB images Detection of vehicle speed from digital aerial images Automated detection of vehicles from aerial images 4

QB images Detection of vehicle speed from digital aerial

5 Time Lag between PAN and MS sensors of QuickBird Images from Japan, USA, Peru, Thailand, Indonesia, Morocco, Iran, Turkey, Algeria T MS -T PAN = t = 0.20s or 0.23s as Etaya et al. (2004) Time lag between sensors Time 5

= 0.20s or 0.23s as Etaya et al. (2004) 0.24 0.

6 QuickBird Images with PAN and MS C ity C ountry Pan D ate Pan second M ultisecond Tim e lag Angle P huket Thailand T04:01: Z B ourm erdes Algeria T10:38: Z N azca P eru T15:17: Z B angkok Thailand T03:53: Z H o C him inh Vietham T03:23: Z A lh oceim a M orocco T10:52: Z A lh oceim a M orocco T10:52: Z Zem m ouri Algeria T10:26: Z Zem m ouri Algeria T10:26: Z Zem m ouri Algeria T10:36: Z ElB ahri Algeria T10:36: Z B ourm erdes Algeria T10:36: Z Zem m ouri Algeria T10:20: Z B ourm erdes Algeria T10:20: Z ElB ahri Algeria T10:25: Z B ourm erdes Algeria T10:25: Z G olcuk Turkey T08:34: Z Java Indonesia T02:53: Z Java Indonesia T02:53: Z B am Iran T06:36: Z B am Iran T06:36: Z B am Iran T06:43: Z B am Iran T06:43: Z A lh oceim a M orocco T10:53: Z A lh oceim a M orocco T10:53: Z A lh oceim a M orocco T10:53: Z N iigata Japan T01:14: Z N iigata Japan T01:14: Z N iigata Japan T01:14: Z N iigata Japan T01:14: Z P huket Thailand T04:12: Z K atrina U SA T16:59: Z Java Indonesia T03:09: Z Java Indonesia T03:14: Z Java Indonesia T03:14: Z Java Indonesia T03:25: Z

219099Z 50.219099 50.419314 0.200215 12.4 H o C him inh Vietham 2003-01-24T03:23:00.199687Z 0.199687 0.428753 0.229066 5.8 A lh oceim a M orocco 2003-05-02T10:52:47.103698Z 47.103698 47.305407 0.

7 HIS Pansharpening Time lag between Pan and MS bands Ghost of MS bands RGB BLUE CYAN GREEN I HSI YELLOW MAGENTA BLACK WHITE GREEN CYAN WHITE BLUE RED MAGENTA RED YELLOW BLACK H S 7

8 Pansharpened QuickBird Image from Google Earth Tokyo/Narita International Airport, Japan just take-off l=17.8 m v= l/ t =17.8/0.2 =89 m/s = 320 km/h 8

9 Pansharpened QuickBird Image from Google Earth Manila International Airport, Philippines just landing l=18.1 m v=18.1/0.2=90.5 m/s = 326 km/h 9

10 Pansharpened QuickBird Image from Google Earth Hong Kong International Airport, China After take-off l=22.1 m v=22.1/0.2=110.5 m/s = 398 km/h 10

11 Pansharpened QuickBird Image from Google Earth Shinkansen train near Toyohashi, Japan 11

12 Pansharpened QuickBird Image from Google Earth Sky-train in central Bangkok, Thailand 12

13 Pansharpened QuickBird Image from Google Earth Tokyo-Nagoya Expressway near Isehara, Japan 13

14 Simulated Pansharpened Image Having Small Time-Lag between Panchromatic and Multi-spectral Sensors t 0 image 0.25m Panchromatic 0.25m Simulated Pansharpened 0.25m t 0 + t image 0.25m Multispectral 1.0m Ghosts appeared in front of cars. 14

25m Panchromatic 0.25m Simulated Pansharpened 0.

15 Contents Time lag in QB images and moving objects seen in Google Earth Visual inspection of vehicle speed from QB images Detection of vehicle speed from digital aerial images Automated detection of vehicles from aerial images 15

16 Pan image Time = T03:53: Z QB bundle product of Bangkok S: se= (ms) (pan)=-3.00m sn= (ms) (pan)=-2.40m S 2 =se 2 +sn 2 = 3.84m T: t= (ms) (pan) 0.20s v: v= ds/dt =3.84/0.20 =19.2 m/s = 69.1km/h Map: E N MS image Time = T 03:53: Z1 Map: E N 16

84m T: t=50.419314(ms)-50.219099(pan) 0.20s v: v= ds/dt =3.84/0.20 =19.2 m/s = 69.

17 Up: North bound L1~L20 (from left) R20~27: the cars aren t on the high way going to the crossroad. Down: South bound R1~R27 (from right) 17

18 North bound lanes Visual inspection results V(km/h) km/h < v < 120km/h V average =97.93km/h 60 Car Nu. V(km/h) South bound lanes Car Nu R1~R19: 45km/h<v<90km/h v average =70.92km/h R20~R27: The speed is reduced as the cars close to the crossroad. 18

V(km/h) South bound lanes 90 80 70 60 50 40 1 3 4 9 14 6 7 13 12 2 8 15 10 11 5 Car Nu.

19 Result of visual speed detection The length of the arrows the speed The arrow directions mean the car directions North bound lanes South bound lanes 19

20 Test of accuracy using simulated QB images Carry out tests using simulated QB images produced from aerial images to assess the accuracy of visual inspection. (use 3 aerial images and move the cars in the images to obtain the second images.) Miyanogi, Chiba, Japan 02/7/20 from PASCO CORP. 0.25m/pixel 20

21 Simulate the Pan image Pan=(Band1+Band2+Band3)/3 Resize to 0.6m/pixel After Simulated Pan image Before Simulated MS image Resize to 2.4m/pixel 21

22 The standard deviation of the speed difference between the reference value and detected results is around 11km/h. The standard deviation of the azimuth angle difference between the reference value and detected results is around 13 degrees. 22

23 Contents Time lag in QB images and moving objects seen in Google Earth Visual inspection of vehicle speed from QB images Detection of vehicle speed from digital aerial images Automated detection of vehicles from aerial images 23

24 4 Panchromatic lenses Digital Aerial Camera: UltraCam D 4 Multispectral lenses

25 Speed detection of vehicles from aerial images High resolution. Two consecutive aerial images have 60~80% overlap. The time lag between two images is a few seconds. The speed of vehicles can be detected with higher accuracy. 25

26 Flight Lines of Digital Aerial Photography of Tokyo by GSI UltraCam D Date: August 4, 2006 Azimuth Overlap: 80% Lateral overlap: 80% Flight height: 1,400m Bands: B,G, R, NIR, Pan Pixel size: 9.0 µm (Pan, MS) Image size: 11500*7500 pixels (Pan) 3680 * 2400 pixels (MS) Resolution: 12 cm (Pan) 37.5 cm (MS) 26

27 Sharpened Multispectral True Color Composite Sharpened Multispectral False Color Composite 27

28 Hamazakibashi, Tokyo 04/8/06 from GSI 0.12m/pixel GPS Time: s GPS Time: s W S W S Δt= = s 28

29 The targets in the upper lanes: 16 cars. The targets in the lower lanes: 21 cars. Transparency 60% 29

30 Upper Roadway Speed (km /h) km/h < v < 50km/h V ave = 39.7 km/h C ar N um ber Lower Roadway Speed (km /h) km/h < v < 30km/h V ave =19.0 km/h C ar N um ber 30

31 The length of the arrows the speed The arrow directions mean the car directions 31

32 Contents Time lag in QB images and moving objects seen in Google Earth Visual inspection of vehicle speed from QB images Detection of vehicle speed from digital aerial images Automated detection of vehicles from aerial images 32

33 Flowchart of automated vehicle extraction RGB Aerial Image Method Road extraction Road image (white background) PAN image Initial extracted result (road and background removed) Final image and data (vehicles and shadows) Noise removing (Morphological filter) Transform Object-based vehicle extraction Verification (vehicles & associated shadows) 33

34 Before vehicle extraction 34

35 After vehicle extraction 35

36 Database (example) Car ID before X/pixel Y/pixel Size/pixel Car ID after X/pixel Y/pixel Size/pixel Shadow ID X/pixel Y/pixel Size/pixel Shadow ID X/pixel Y/pixel Size/pixel A shadow object has the same ID as a car s means it is cast by this car. The last ID of shadow means the number of cars in the scene. The number of cars is not the same, because of the noise. 36

37 Flowchart of speed detection Parameters Result 37

38 Reference data No moving, so It s not a car. Result of speed detection The detected result shows good agreement with the reference data. 38

39 Summary The time lag between Pan and MS bands of QB images was highlighted to detect moving objects. The speeds of vehicles were visually detected from QB images, but accuracy was not very high. The speeds of vehicles were detected from digital aerial images with high accuracy. An object-based automated method of vehicle extraction from aerial images was proposed. 39

Green = 0,255,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (43,215,35) Equal Luminance Gray for Green

Green = 0,255,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (43,215,35) Equal Luminance Gray for Green Red = 255,0,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (184,27,26) Equal Luminance Gray for Red = 255,0,0 (147,147,147) Mean of Observer Matches to Red=255