Slide 1Scope of the ProjectSpatio temporal moving object detection:Slide 4Parametric modelSlide 6An example to find P(RGB/color)Slide 8Non-parametric EstimationSlide 10Experimental Results- Spatiotemporal object detectionSlide 12Slide 13Compressed Domain Object detectionSlide 15Slide 16Slide 17Slide 18Slide 19Finding motion from frame to frameSlide 21Slide 22Slide 23Slide 24Problems/ Constraints in object detection using motion vectorsGUI for annotating hand locations:Accuracy of DetectionResults:Further ImprovementsReferencesSlide 31Slide 32Statistical analysis and evaluation of spatio-temporal and compressed domains moving object detectionPresented by Rajesh RadhakrishnanInstructor: K.R RaoScope of the Project•Three essential modules are required to obtain the comparative study of the moving object detection between two domains.• First is the manual annotation of hand locations using a GUI to get the co-ordinate location of the object in every frame.• Second is to obtain time series of hand locations based on spatio-temporal algorithm.• Third is to obtain time series of hand locations based on compressed domain algorithm.Spatio temporal moving object detection:• Given a video test sequence, we need to perform background subtraction to separate the foreground moving objects from the background model.Fig 1: Block diagram of steps involved in spatio temporal object detection•Background modeling is a process of obtaining static image regions from a sequence of video.• Frame differencing is one of the technique to perform background modeling. •Parametric and non-parametric estimation model is a way to improve the candidate foreground object detection.Parametric model• Simple Gaussian model is an example of a parametric model. Estimate parameters such as mean and standard deviation.• Consider a block of ground truth image and estimate the mean and standard deviation of the block. • Ground truth can be defined as the area under the actual moving object in each frame.•P(color/RGB)=(P(RGB/color)*P(color))/P(RGB) by Bayes rule.• Where, P(color/RGB)= conditional probability and P(x) =probability of x.•P(RGB/color) is estimated from the training set, which is the Gaussian probability of RGB/color(mean ,std), where std = standard deviation.• Assume P(R), P(G), P(B) are mutually independent, then Gaussian probability of P(RGB/color) is given by, P(RGB/color)=P(R/color)*P(G/color)*P(B/color) where P(R/color(mean, STD))= Gaussian_probability(R(i,j),mean,std)• where, 1≤i≤N, 1≤j≤M, of an image of size NxMAn example to find P(RGB/color)•Consider the sub-block image shown in Fig 1 to estimate the mean and standard deviation of the green color object to be detected. This is an sub-block image of size 80x60, find the mean and standard deviation of each color band separately. Fig 2:Sub-block image to estimate mean and standard deviation.• How to estimate P(color) and P(RGB) ??• P(color) can be of any value, this determines the color adjustment factor.• Some of the prior probabilities are shown,P(RGB) =1, as shown below,P(RGB)= P(RGB/color)* P(color)+P(RGB/non_color)*P(non_color)X XXP(x)P(x)P(x)Prior ProbabilitiesFig. 2.1 Prior probability distribution [4]Fig. 2.2 Prior probability distribution [4]Fig. 2.3 Prior probability distribution [4]Non-parametric Estimation• Non- parametric model does not require any parameter estimates.• Histogram based distribution is one of the non parametric model.• For basic color object detection like red, green and blue colors, approximation based method can be done.•Suppose a green object has to be detected, given a color image of dimension NxMx3,•Subtract the green pixel region with the rest to get the probability of green distributions in the image Green_dist(i,j)=2*Image(i,j,2)-Image(i,j,1)-Image(i,j,3)Experimental Results- Spatiotemporal object detection• The spatio-temporal moving object detection algorithm was tested on two video sequences one with a nearby object and another with a far end object.• Four set of output were generated, with single and multiple detection boxes.Fig 3. Close up video Frame # 17, 3 detection boxes.Fig 4. Distant video Frame # 19, 3 detection boxes.Object detection output of a frame with three detection boxesFig 5. Close up video Frame # 19, 1 detection boxFig 6. Close up video Frame # 17, 1 detection boxObject detection output of a frame with single detection boxCompressed Domain Object detection• Motion vector estimate is used to predict the moving object block.Algorithm:• Rearrange the frames from bit stream order to display order.• Consider three pairs of arrays present, past and future for storing the motion vectors.• The process of inputting the motion vectors into correct arrays and reordering frames were incorporated into the decoder.•Each video sequence is divided into one or more group of pictures (GOPs), the display order of the GOPs will be of the form given in fig . 7, • Here I , B and P are intra-coded, bidirectional prediction and predicted frames.Fig 7: MPEG group of pictures – Display order [11].But the encoder output in bit stream order will be of the form I P B B P B B I B B P B B. [11]Bit stream to display order conversionConverting from bit stream order to Display orderFig.8. Block diagram illustrating conversion from bit stream order to display order [11].• If an P frame is encountered, place it in a temporary storage called future.• P frame will be left in the future until another I or P frame comes in, on arrival of a new I or P frame, the already existing I or P frame is removed from the future and put in the display order.• All B frames are immediately put in display order.•Next step is to obtain the motion vectors from these frames.Fig.9. Flow chart of the operational program. [9]Frame handling- Program Operation•Each incoming frames are placed in a past, present or future array locations based on their type (i.e) either a P, I or B frame.•The size of the array will be equal to the frame size in macro blocks, (i.e) the frame size used in this project is 240x320, for a motion vector of block size 8x8, array size would be 30x40.• Once the motion vectors are stored, the next step is to find the motion from frame to frame.Finding motion from frame to frame• The output of the present and past frame array motion vectors are used to find the motion from frame to frame.Past Present Vector types that
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