Unlock instant, AI-driven research and patent intelligence for your innovation.

An Animal Counting Method Based on Graph Regularized Optical Flow Attention Network

A counting method and attention technology, applied in computing, computer components, instruments, etc., can solve the problems that affect the accuracy of optical flow estimation, not suitable for data sets, etc., and achieve the effect of various animal species

Active Publication Date: 2021-05-14
TIANJIN UNIV
View PDF13 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The general method is to use a pre-trained optical flow estimation network to extract the optical flow, and then fix the optical flow network during network training, but this method is not suitable for a specific data set and may also affect the accuracy of optical flow estimation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • An Animal Counting Method Based on Graph Regularized Optical Flow Attention Network
  • An Animal Counting Method Based on Graph Regularized Optical Flow Attention Network
  • An Animal Counting Method Based on Graph Regularized Optical Flow Attention Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] An embodiment of the present invention provides a method for counting animals based on graph regularized optical flow attention network, see figure 1 , the method includes the following steps:

[0038] 101: Graph regularized flow attention network (GFAN) consists of three parts: shared feature encoder module, count decoder module and temporal consistency module, the network structure diagram is as follows figure 2 shown.

[0039] Among them, the shared feature encoder module uses the VGG-16 network [1]The first four sets of convolutional layers in are used as the backbone to extract the feature map (Feature Map) on two different frames t and t+τ, and then use the online optical flow network based on warping loss to capture the difference between the two frames. sports information. To obtain enhanced feature maps, the generated optical flow is used to warp the feature map of the (t+τ)th frame to the tth frame, where the parameter τ determines the temporal distance be...

Embodiment 2

[0045] The scheme in embodiment 1 is further introduced below in conjunction with specific examples and calculation formulas, see the following description for details:

[0046] 1. Data preparation

[0047] The present invention adopts the video-based large-scale animal count data set AnimalDrone collected by unmanned aerial vehicle in this method during training, and this data set is made up of two subsets, namely AnimalDrone-PartA and AnimalDrone-PartB, and the scene of data set covers different The scene, the variety of animals. After data pruning and annotating the data, AnimalDrone-PartA contains 18,940 images and 2,008,570 annotated objects including training and testing sets, and AnimalDrone-PartB includes 103 video clips including training clips and testing clips, totaling 34704 frames and 2,040,598 annotated objects. Can be used for testing of various target counting methods

[0048] 2. Online optical flow network based on warping loss

[0049] Due to the consider...

Embodiment 3

[0077] The experimental results 1 used in the embodiment of the present invention are shown in Table 1. This result shows the counting evaluation results of various latest methods on the AnimalDrone two-part A and B datasets, including MCNN [2] , MSCNN [3] , CSRNet [4] and other methods as well as the method in the present invention. All counting methods are trained on the training set and evaluated on the test set. The results show that the method used in the present invention can generate more accurate density maps in different situations, and achieve better performance than other methods. Experimental results reflect that the method used in the present invention is superior to existing methods.

[0078]The experimental results 2 used in the embodiment of the present invention are shown in Table 2. The results show the evaluation results of the three variants of GFAN, GFAN-w / o-graph, GFAN-w / o-warp and GFAN-w / o-cnt on the entire AnimalDrone dataset test, in order to bett...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention discloses a method for counting animals based on a graph-regularized optical flow attention network, including: a shared feature encoder extracts feature maps on frames t and t+τ, and uses an online optical flow network based on warping loss to capture two frames The motion information between them; use the generated optical flow to warp the t+τ frame feature map to the tth frame; the time consistency uses the warping loss calculation for the obtained feature map to obtain the error between the feature map and the original feature encoder; count decoding The detector applies deconvolution Deconv step by step to optical flow warping to generate feature maps. Add semantic features with lateral connections at each scale, apply a 1×1 convolutional layer to obtain an intermediate density map, and use a mean square loss function on each scale density map; each scale feature after adding semantic features For fusion, a multi-granularity loss function is used to reduce the generation of errors. A 1×1 convolutional layer is used to generate the final density map, and graph regularization is used by temporal consistency to further enhance temporal relationships.

Description

technical field [0001] The invention relates to the field of object counting, in particular to an animal counting method based on a graph regularized optical flow attention network. Background technique [0002] The world of artificial intelligence (AI) is rapidly emerging and is now being used in fields such as agriculture and wildlife conservation. For example, drones equipped with cameras can be used to detect crop diseases, identify crop maturity, and monitor animal movements; in addition, drones are also very suitable for animal tracking and group counting. Observe the problem of mutual occlusion between individuals in high-density groups in the view. Although UAVs have made great progress in the field of target counting in recent years, due to problems such as target motion blur, various scale changes, sparse positive samples, and small target objects, counting animals in the information captured by UAVs is still of great importance. challenge. [0003] Currently, t...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/40G06F18/253G06F18/214
Inventor 朱鹏飞魏志强翁哲明彭涛曹亚如胡清华
Owner TIANJIN UNIV