Projects:2015s2-216 Feral Cat Detector - Revision history

A1656956 at 09:01, 30 May 2016

2016-05-30T09:01:01Z

2016-05-30T08:57:16Z

2016-05-30T08:53:35Z

2016-05-30T08:49:29Z

2016-05-30T08:47:17Z

2016-05-30T08:45:45Z

2016-05-30T08:44:50Z

2016-05-30T08:44:20Z

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 The first classification table below shows the performance of the system under a multiclass recognition scenario. Around 85% of cats are correctly classified. For other species the misclassification rate as ‘cat’ is below 4% which is quite promising.
 [[File:p4.jpg]]
 The smaller table shows the performance as a binary classifier. The performance is much better with 95% correctly classified.
 [[File:p5.jpg]]
 '''6 Acknowledgement'''
 ----

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 We use binary support vector machine classifier as classification model to divide global features of separate categories in feature space at training stage. Therefore, the new examples from in-field capturing could be classified to to either of two classes depending on which side of hyperplane they fall onto.
-Identification Results
+'''5 Identification Results'''
 ----
 The first classification table below shows the performance of the system under a multiclass recognition scenario. Around 85% of cats are correctly classified. For other species the misclassification rate as ‘cat’ is below 4% which is quite promising.
 The smaller table shows the performance as a binary classifier. The performance is much better with 95% correctly classified.
-Acknowledgement
+[[File:p5.jpg]]
 ----
 For allowing us to capture data on a variety of animals and cats, we thank Byron Manning at Cleland Wildlife Park, Rob & Lynn Walker, Chris & Julia Lawson, Catherine Lawson & Jarrod McWilliams, Penny Williamson, and Dr. Danny Gibbins.
-Further thanks to Dr Danny Gibbins and Dr Said Al-Sarawi for help and support throughout the year.
+Further thanks to Dr Danny Gibbins and Dr Said Al-Sarawi for great help and support throughout the year.

← Older revision		Revision as of 08:53, 30 May 2016
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	We use binary support vector machine classifier as classification model to divide global features of separate categories in feature space at training stage. Therefore, the new examples from in-field capturing could be classified to to either of two classes depending on which side of hyperplane they fall onto.		We use binary support vector machine classifier as classification model to divide global features of separate categories in feature space at training stage. Therefore, the new examples from in-field capturing could be classified to to either of two classes depending on which side of hyperplane they fall onto.
		+
		+	5 Identification Results
		+	----
		+
		+	The first classification table below shows the performance of the system under a multiclass recognition scenario. Around 85% of cats are correctly classified. For other species the misclassification rate as ‘cat’ is below 4% which is quite promising.
		+	The smaller table shows the performance as a binary classifier. The performance is much better with 95% correctly classified.
		+
		+	6 Acknowledgement
		+	----
		+	For allowing us to capture data on a variety of animals and cats, we thank Byron Manning at Cleland Wildlife Park, Rob & Lynn Walker, Chris & Julia Lawson, Catherine Lawson & Jarrod McWilliams, Penny Williamson, and Dr. Danny Gibbins.
		+
		+	Further thanks to Dr Danny Gibbins and Dr Said Al-Sarawi for help and support throughout the year.

@@ Line 51: / Line 51: @@
 To preserve the spatial information of the image and linearly classify image data in feature space, we apply spatial pyramid matching kernel on image which basically partitions image into a number of spatial subregions, in which derived sparse codes will be combined together to generate one global feature.
 '''4.4 Classification'''
 We use binary support vector machine classifier as classification model to divide global features of separate categories in feature space at training stage. Therefore, the new examples from in-field capturing could be classified to to either of two classes depending on which side of hyperplane they fall onto.

@@ Line 39: / Line 39: @@
 '''4 Image Representation and Classification'''
 '''4.1 Feature Extraction'''
 At first, dense-SIFT feature descriptor is extracted on patches across the image as local feature.
 '''4.2 Sparse Coding'''
 Then, sparse coding technique is applied on extracted dense-SIFT features in order to represent them with weighted linear combination of basis vectors (can be seen as stimulus patterns from dataset) in learned dictionary.
 '''4.3 Spatial Pyramid Matching'''
 To preserve the spatial information of the image and linearly classify image data in feature space, we apply spatial pyramid matching kernel on image which basically partitions image into a number of spatial subregions, in which derived sparse codes will be combined together to generate one global feature.

@@ Line 31: / Line 31: @@
 •Classification against database of captured images using support-vector machine
 Our came trap, as shown below, can easily be set up in a various environmental conditions since it does not require much space and also can be adjustable according to specific requirement.
 [[File:p2.jpg]]                  [[File:p3.jpg]]
 A layout of the embedded system is shown at right hand side, attached with devices including IR and thermal cameras (capturing devices), IR lamp (illuminate when detection register), batteries (in-field power supply) and USB stick (storage of captured images).

← Older revision		Revision as of 08:44, 30 May 2016
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	[[File:p1.png]]		[[File:p1.png]]
−	~~----~~

	As shown above, the system process can be break down to following procedures:		As shown above, the system process can be break down to following procedures:

← Older revision		Revision as of 08:44, 30 May 2016
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	As shown above, the system process can be break down to following procedures:		As shown above, the system process can be break down to following procedures:
−	~~----~~
−

	•Data collection in the field using Raspberry Pi 2		•Data collection in the field using Raspberry Pi 2

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 As shown above, the system process can be break down to following procedures:
 ----
 •Data collection in the field using Raspberry Pi 2
 •In-field real-time processing of thermal data to detect animals
 •Capture images using thermal and infrared cameras
 •Background tracking using data-fusion of thermal and IR images
 •Background subtraction, segmentation, and cropping isolates the animal
 •Feature extraction using dense SIFT
 •Dictionary matching, sparse coding and spatial-pyramid matching creates one global feature per image
 •Classification against database of captured images using support-vector machine