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Real-Time Detection of Multiple Moving Objects in Complex Image Sequences. International journal of imaging systems and technology 10,305-317,1999. Speaker: M. Q. Jing. Outline. Introduction Change Detection Algorithms
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Real-Time Detection of Multiple Moving Objects in Complex Image Sequences International journal of imaging systems and technology 10,305-317,1999 Speaker: M. Q. Jing
Outline • Introduction • Change Detection Algorithms • Simple difference method • Derivative model • Shading model • LIG model • Binary Statistical Morphology • Multiple object detection • Result & Conclusion
Introduction(1) • Change detection (CD) is an process for many machine vision systes • Track moving object • Analyze the traffic flow • Guide autonomous vehicles • Task: finding significant differences between images • Difference may be caused by • Motion of the camera • Entrance or exit of an object from the scene • Change in illuminations & Bad environmental conditions
Introduction(2) • CD is performed at pixel, edge or higher feature levels. • Feature levels require more computational effort • CD take two digital images as input. The output is a binary image
Change Detection Algorithms --Simple Difference(SD) method • Input: Two N x N digitized images It(x,y), It-1(x,y) • Output: Binary image Bt(x,y) • Dt(x,y)=| It(x,y), It-1(x,y) | Fig 2: a,b
Change Detection Algorithms --Derivative Model (DM) Fig 2: c,d
Change Detection Algorithms --Shading Model (SM) • The intensity Ip at point (x,y) Ip= Ii * Sp , where Ii = illumination Sp = shading coefficient • Calcuate the varance of the intensity Ip/Ip-1 0 => no changed • Fig2 (e,f)
Change Detection Algorithms -- Local intensity gradient(LIG) method • Pixels at the location having a high gray-level gradient form a part of an object • Nearly pixles having similar gray levels will be also part of the same object. • Large negative gradients at pixels belonging to object boundaries
Change Detection Algorithms -- Local intensity gradient(LIG) method • 1: I -> G(I) • 2: G(I) -> m x m blocks • Limit the effects of illumination changing • 3: for each block, calcuating the mean & variance • 4: regional means and variances are smoothed using the neighboring regions • 5. then interpolated to fill an m x m again • 6 compare with background and foregound Fig 2: I,l
Binary statistical morphology • Mathematical morphology(MM) describe images as sets and image-porcessing operators. • The main drawback of such operators is the high sensitivity to noise • Statistical morphology provides a noise-robust probabilistic generalization of MM.
Binary statistical morphology • SM interprets erosion and dilation as probabilistic lta and wta operator • Statistical erosion & statistical dilation = minimum variance estimators of the ouput distribution, P(H|I)
Binary statistical morphology • SD computed at an image location m as: • Binary Statistical Dilation(BSD) is obtained by threshold SD at value
Multiple object detection It(x,y) BCKt(x,y) Simple Difference Dt(x,y) First Level Ki bata Binary Statistical Erosion with TSE Fig 4: a,b Bt1(x,y)
Multiple object detection -- The first step • Temporal structuring element (TSE) = 3x3 mask • A low ki value should be selected to maximize the probability of detecting moving object points. • The first level is represent the pixel where are object or background.
Multiple object detection -- The Second step (noise reduction) Bt1(x,y) Binary Statistical Erosion with TSE1 Zt(x,y) Set filtering Yt(x,y) Binary Statistical dilation with TSE2 Bt2(x,y)
Multiple object detection -- The Second step (noise reduction) • (a) elimnate isolated points • (b) (c) taking into account the compactness constraint by favoring dense agglomerate of changed pixels. Fig 4. c,d
Multiple object detection -- The Third step (blob tracking) • Blob tracking is a difficult task • Many blobs may appear or vanish in successive frames • Blobs may be occluded by overlapping of other blobs • Problems are caused by the high dimensionality of the set of possible combinations • See Fig 4. e
Multiple object detection -- The Third step (blob tracking) • Step1: a blob is not expected to be too far from where it was in the previous frame. • Choose a mask,(2u*cosa+1 ) in x direction and 2u*sina+1 in y direction • The mask is centerred in I’s barycenter • The blobs in I+1 frame whose barycenter fall within this mask are selected.
Multiple object detection -- The Third step (blob tracking) • Step2: Let CBt+1(bi)= set of blobs on the frame t+1 which are candidate to match with the blob bi detected on the frame t => 給定一blob bi 選擇在下一個frame,落在 bi範圍內的blob 集合
The cost function * 當 blob 大小不變時,cost function值最小
Result-- The detection performance • Tests on Outdoor Scenes • Fig 5. • The detection performance • P=(correctly identified #) / (the method detected #) 即在此方法所有detected的blob中,有幾個是正確的. • R=(correct detected #)/ (original image #) • G={(correctly identified #) -(false detected #)}/ (original image #) Fig 7.
Result-- Overflow error & gap error • r(i)=1, if i belong to the • detected blob • rgr(i) =1, if i belong to • the manually extracted • blobs Fig 9
Result-- Test on Noisy Images • A random noise with uniform distrubution was added to some frames of the image sequence. • Fig 10(a) • SD and SM method could not be applied to very noisy images. • Fig 10(b) is proposed method result. • Fig 11(a) is different noise level result
Result-- Time performance • The time performance of the different CD methods were compared. • SD and proposed method are 10 times fast than the SM and DM • 15 times faster than the LIG method
Conclusion • If the system works outdoors and in badly illuminated enviroments, => LIG , the proposed method • If real-time is required, => the proposed method
SD method DM method Fig 2
Fig 2: LIG Result
Fig4: the proposed result
