Moving vehicle discrimination using Hough transformation

Yuki Okamoto, Isamu Matsunami, and Akihiro Kajiwara

Graduate School of Environmental Engineering, The University of Kitakyushu,1-1 Hibikino,Wakamatsu, Kitakyushu, Fukuoka, 808-0135, Japan

Abstract— In this paper, a vehicle detection scheme issuggested using Hough transformation where some movingvehicles are discriminated from significant clutter by usinglinear trajectory detection in the Hough space. In general, thereceived range profile for a wideband radar pulse includesmany echoes from various obstacles such as guardrail.Observing the profiles with high PRF during a short duration,each echoes trajectory can be estimated employing Houghtransform. For example, the trajectory is regarded as linearwhen the speed of echo is constant and the moving vehiclesare spatially discriminated with clutter. The Doppler can alsobe estimated from the time-range coordinate. As a result,some significant clutters would be eliminated in the Houghspace and the vehicles are expected to be detected andtracked with high range and Doppler resolution. The fieldmeasurement at 24GHz was conducted for moving vehiclesand the usefulness is discussed for various scenarios.

Index Terms— UWB, radar, clutter, hough transform

I. INTRODUCTION

Wideband or ultra-wideband radar with high range-resolution has attracted much attention for use in motorvehicle, because it offers various applications such as pre-crush warning, stop-and-go operation, and lane changeassist. However, the radar echo contains unwanted echoescalled clutter, which make it difficult to detect multiplevehicles. Especially, short-range/wide-angle vehicle radarsuffers from significant clutter unlike a long-range/narrow-angle radar used for automatic cruise control (ACC) [1].It is therefore expected to detect multiple moving vehiclesusing the Doppler. The resolutions of a range and Dopplermeasurement are dependent on the signal bandwidth andduration, respectively [2]. For example, a shorter pulsesignal has better range-resolution but poorer Doppler res-olution. Therefore, the requirement for range and Dopplerresolution are in conflict. However the Doppler may accu-rately be measured by observing the range profile of highrange-resolution radar echo in time domain, that is, thetime-range profile. The Doppler detection scheme has notbeen presented for wideband vehicle radar.

In this paper, a vehicle detection scheme is suggestedusing Hough transformation where some moving vehiclesare discriminated from significant clutter by using lineartrajectory detection in the Hough space. In general, thereceived range profile for a wideband radar pulse includesmany echoes from various obstacles such as guardrail. Ob-serving the profiles with high PRF during a short duration,

each echoes trajectory can be estimated employing Houghtransform. For example, the trajectory is regarded as linearwhen the speed of echo is constant and the moving vehiclesare spatially discriminated with clutter. The Doppler canalso be estimated from the time-range coordinate. As aresult, some significant clutters would be eliminated in theHough space (please note that Doppler of static cluttercorresponds to the speed of measurement vehicle) andthe vehicles are expected to be detected and tracked withhigh range and Doppler resolution. The field measurementat 24GHz was conducted for moving vehicles and theusefulness is discussed for various scenarios.

II. RANGE PROFILE AND HOUGH TRANSFORM

A. Range profile

The radar echo is generally presented by multiple pulseswith gains (βk) and propagation delays (τk), where k is thepath index. Suppose a nanosecond of s(t), the range profileof received echoes, y(τ ,t), is the time convolution of s(t)and the impulse echo response as follows;

y(τ, t) =∑

k

βks(t − τk). (1)

Fig.1 shows an example of received power range profilefor a nanosecond pulse on a roadway. It is seen thatthe range profile includes many echoes distinguishablewith different delay. Detection, tracking and recognitionof vehicle in clutter are very important issues in vehicularradar systems. Traditionally, the received range profile foreach radar pulse is compared against a given threshold anda detection decision is made. Once the decision is done, therange profile is discarded and the next one is considered.This is called threshold detection. For wideband or ultra-wideband radar, however, it should be difficult to discrim-inate the vehicle from clutter as shown in Fig.1. This isbecause guardrail and other constructed objects on roadcan result in significant echoes. The frequency-domainbased detection scheme may also not be applicable becauseof the poor Doppler resolution. However, some movingvehicles can be detected against clutter using the time torange profile. Fig.2 shows the range profiles as a functionof transmitted PR number (pulse repetition number), whichis called time-range profile. It is seen from Fig.2 that eachechoes trajectory may be estimated and the Doppler is alsocalculated. The Doppler resolution depends on the range

978-1-4244-7685-5/11/$26.00 © 2011 IEEE RWS 2011367

Fig. 1. Power range profile for a roadway

Fig. 2. Time-range profiles for 80 transmitted pulses

bin or radar bandwidth. Please note that each static clutterexperiences a same Doppler unlike moving vehicles.

B. Hough transform

Hough transform (HT) has been widely applied fordetecting motions in the fields of image processing andcomputer vision. Consider the time-range profile as shownin Fig.2, the trajectory of each echo can be estimated bythe HT, which is a computationally efficient algorithm inorder to detect the desired vehicle or target in time-spacedata map. For example, the trajectory would be linear fora short duration of 0.1 second or less and the Doppler isestimated by the inclination of linear line. The HT linesof moving vehicle are overlaid on the time-delay profilesof Fig.2. It is seen that the static clutter can be identifiedsince the inclination corresponds to the Doppler of movingmeasurement vehicle.

III. VEHICLE DISCRIMINATION

A. Measurement set-up and procedure

The measurements were conducted on the roadway withbuildings in our campus, Kitakyushu University, as shown

(a) Measurement scene

(b) Measurement scenario

Fig. 3. Measurement scenario

in Fig.3. The detail specification is shown in Table I. Avector network analyzer (VNA) with time-domain functionwas used for measuring the frequency response where aninverse fast-Fourier transform (IFFT) was taken in orderto derive power range profile of the radar echo fromthe frequency-domain data. For the measurement with anormal windowing function, the range-bin (6dB pulse-width) can be approximated by [3]

ΔR =1.96 · cBW

, (2)

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TABLE IMEASURMENT PARAMETERS.

System Vector Network AnalyzerBandwidth 500MHz,300MHz

(Center frequency:24GHz)Antena Polarization H-H

Type Double-ridge Horn antenaGain 12.5dBi(24GHz)Height 60cm

Target Sedan:�1 L:4640mm W:1720mm H:1340mmSUV:�2 L:4420mm W:1810mm H:1695mmMini-van:�3 L:4580mm W:1695mm H:1850mm

where BW is the bandwidth and c is light velocity. Forexample, the range-resolution is approximately 0.6m and1.0m for a bandwidth of 500MHz and 300MHz respec-tively. The four vehicles were driven along the roadwayand the received signals were processed on board. A pulserepetition interval (PRI) of 15ms is considered for thescenario of Fig.3. The antennas with the beam-width 70◦ inhorizontal direction were placed 60cm above the ground.Please note the anti-collision radar is designed for short-range/wide-angle object detection unlike the ACC radaroperating at 77GHz.

B. Measurement results

Fig.4 shows the HT algorithm from time-range profileto some trajectory lines where L is the segment number.And the quasi-images for BW=300MHz and 500MHz areshown in Fig. 5(a) and 5(b) respectively. Many trajectoriesshould be plotted through the image using the Hough spacetranslation. The number of trajectory lines depends on thesignal-to-clutter ratio and the window size to observe thetime-range profile. Some trajectory lines of a time-rangeprofile should be connected to the lines of the followingprofile, thereby the trajectories of moving vehicle andclutter are detected. Fig.6 (a) shows the lines estimated bythe HT for the BW of 500MHz, and Fig.6 (b) the showsthe detected lines by the suggested algorithm shown inFig.4 where consecutive two time-range profiles are used.It is seen that moving vehicles and clutter are discriminatedfrom the Doppler. Fig.7 also shows the detected lines forthree moving vehicles and clutter for the BW of 500MHz.The results of Figs. 6 and 7 are found to agree withthe scenarios. The measurements were also conducted fordifferent scenarios of side-looking and back-looking radarand the suggested scheme is found to be useful.

Fig. 4. Signal flow for HT algorithm

(a) BW=300MHz

(b) BW=500MHz

Fig. 5. Quasi-images of time-range profile

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(a) Example of detected line by Hough Transform

(b) Detected lines by our suggested HT algorithm

Fig. 6. BW=300MHz

Fig. 7. Detected line by our suggessted HT algorithm(BW =500MHz)

IV. CONCLUSION

Wideband or ultra-wideband radar has attracted muchattention for use in motor vehicle because it offers var-ious applications such as pre-crush warning and lanechange assist. However, the radar echo contains significantclutter from guardrail and other constructed objects on

road, which makes it difficult to detect multiple vehi-cles. Especially, short-range/wide-angle vehicle radar at24GHz suffers from significant clutter unlike the long-range/narrow-angle radar used for ACC. It is thereforeexpected to detect some moving vehicles under clutter. Inthis paper, a vehicle detection scheme has been suggestedusing a Hough transformation where each vehicle canbe discriminated from clutter by using linear trajectorydetection in the Hough space. The Doppler can also beestimated from the time-range coordinate. As a result,some significant clutters are eliminated in the Houghspace and the vehicles are detected and tracked with highrange and Doppler resolution. The field measurement at24GHz was conducted for some moving vehicles and theusefulness has been discussed for various scenarios.

ACKNOWLEDGMENT

The authors wish to acknowledge member of Kajiwaralab for their helpful comments and discussions.

REFERENCES

[1] M. Skolnik, “Introduction to Radar systems, 3rd ed.” McGraw-Hill,2001.

[2] I. Matsunami, Y. Nakahata, K. Ono and A. Kajiwara, “EmpiricalStudy on Ultra-wideband Vehicle Radar,” in Proc. of IEEE VehicularTechnology Conference, 8G-5, Sept.2008.

[3] “Network analyzer 8722ET userfs guide,” Agilent technologies,2001.

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