CN112541449A - Pedestrian trajectory prediction method based on unmanned aerial vehicle aerial photography view angle - Google Patents

Pedestrian trajectory prediction method based on unmanned aerial vehicle aerial photography view angle Download PDF

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CN112541449A
CN112541449A CN202011505987.4A CN202011505987A CN112541449A CN 112541449 A CN112541449 A CN 112541449A CN 202011505987 A CN202011505987 A CN 202011505987A CN 112541449 A CN112541449 A CN 112541449A
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刘昱
王天保
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Abstract

The invention discloses a pedestrian trajectory prediction method based on an unmanned aerial vehicle aerial photography view angle, which comprises the following steps of 1: the method comprises the steps that pedestrian positions are obtained through a target detection algorithm in pedestrian track preprocessing, and a pedestrian position sequence within a period of time is quickly obtained through a target tracking algorithm; step 2: the track coding uses a long-short term memory network to code a track sequence with a period of time to obtain track motion characteristics; and step 3: the graph convolution network interaction construction takes each pedestrian coordinate as a vertex of the graph convolution network, and the graph convolution network is used for constructing an interaction relation among pedestrians to obtain a track interaction characteristic; and 4, step 4: optimizing maximum mutual information; and 5: and decoding the track motion characteristics and the track interaction characteristics by using a long-term and short-term memory network to obtain a prediction sequence with a certain duration, and completing the track prediction. Compared with the prior art, the method and the device have the advantages that the technical effect of constructing the interaction mode among the pedestrians and predicting the track is achieved, and the robustness is good.

Description

Pedestrian trajectory prediction method based on unmanned aerial vehicle aerial photography view angle
Technical Field
The invention relates to the field of intelligent robots and unmanned platforms, in particular to a pedestrian trajectory prediction method based on an unmanned aerial vehicle aerial photography view angle.
Background
In dense pedestrian scenes such as urban streets and the like, self paths of moving bodies such as automatic driving vehicles and robots need to be planned according to the positions of other pedestrians, safe distances can be kept and risk factors are eliminated through position prediction of targets, and the accuracy of future position prediction of the pedestrians is very important for a decision-making system of the moving bodies. The pedestrian trajectory prediction is a complex task, as the motion habits of each pedestrian are naturally different, and the group environment has human-human interaction, the motion mode of the individual is influenced by the hidden effect of the pedestrians around, people can adjust the route of the individual according to the common knowledge in the aspect of social rules, and the motion subject needs to predict the actions and social behaviors of other people. The construction of pedestrian interaction patterns with high interpretability and generalization capability is the key point of the trajectory prediction problem.
The intensive pedestrian scene at road surface visual angle has a large amount of scheduling problems of sheltering from to ordinary monocular camera is very limited to the judgment ability of distance, and unmanned aerial vehicle can obtain pedestrian's horizontal position information in a flexible way, consequently uses the unmanned aerial vehicle visual angle of taking photo by plane can obtain pedestrian's position and carry out the orbit prediction work high-efficiently.
In the existing computer vision method, the graph neural network applies deep learning on a non-Euclidean structure, constructs the relation between vertex and edge representation objects, shows good robustness and interpretability, and is an effective mode for modeling an interaction mode between pedestrians through a graph topological structure.
Disclosure of Invention
In consideration of the advantages and problems of the convolution network in the establishment of an interaction model, the invention provides a pedestrian trajectory prediction method based on an unmanned aerial vehicle aerial photography visual angle, and realizes a new convolution neural network trajectory prediction model based on the unmanned aerial vehicle aerial photography visual angle, so that an interaction mode among pedestrians is established and trajectory prediction is carried out.
1. The invention discloses a pedestrian trajectory prediction method based on an unmanned aerial vehicle aerial photography view angle, which is characterized by comprising the following steps:
step 1: carry out pedestrian's orbit preliminary treatment in the pedestrian video of unmanned aerial vehicle aerial photography, including fixing a position the pedestrian fast, the central point who gets the target frame promptly is pedestrian's position, establishes all orbit coordinates X of surveing the pedestrian and is X ═ X1,X2,…,Xn；
Step 2: and (3) carrying out pedestrian track coding: representing the relative position change of a single pedestrian track between the previous frame and the next frameComprises the following steps:
Figure BDA0002844966000000021
encoding to fixed length motion vectors using long short term memory networks
Figure BDA0002844966000000022
Then using long-short term memory network to encode to obtain the trace motion characteristics
Figure BDA0002844966000000023
And step 3: constructing graph convolution network interaction: using graph structure Gt＝(Vt,Et) Establishing an interactive model among pedestrians at the time t, and taking the pedestrians as a set V of vertexes in a graph structuretThe interaction relation among the pedestrians is a set E of edgestThe vertex V in each time pointtConnection relation E oftExpressed as adjacency matrix AtWill adjoin the matrix AtEdge of (1)
Figure BDA0002844966000000024
Weights assigned according to different distances
Figure BDA0002844966000000025
Expressed as:
Figure BDA0002844966000000026
characteristics of the path movement
Figure BDA0002844966000000027
Input features as vertices in graph convolution networks
Figure BDA0002844966000000028
Overlapping two layers of graph convolution networks, and obtaining the output characteristic of the ith track through a two-layer GCN structure
Figure BDA0002844966000000029
Output characteristics of pair-to-figure convolution network
Figure BDA00028449660000000210
Carrying out long-short term memory network coding to obtain the track interaction characteristics
Figure BDA00028449660000000211
And 4, step 4: the method for realizing the maximum mutual information between the local features and the global features of the track interaction features comprises the following specific processes: firstly, making negative sample of convolution network input
Figure BDA00028449660000000212
Obtaining output by a graph convolution network
Figure BDA0002844966000000031
Simultaneous extraction of global features
Figure BDA0002844966000000032
The judger D is then trained so that it can output negative examples
Figure BDA0002844966000000033
Misjudging and matching the output Z of the positive sample, thereby training the loss function L of the discriminatorinf，LinfExpressed as:
Figure BDA0002844966000000034
through the training process, the extraction result of the graph convolution network is optimized;
and 5: and (3) carrying out track prediction: using long and short term memory network to characterize trajectory motion
Figure BDA0002844966000000035
And trajectory interaction features
Figure BDA0002844966000000036
Decoding is carried outOutputting a frame of two-dimensional pedestrian trajectory prediction, and determining whether the total output length reaches the prediction sequence length? If not, adding a new output frame into the input sequence, discarding the input of the first frame, if so, outputting the prediction sequence, thereby obtaining the prediction sequence with a certain time length and completing the track prediction.
Compared with the prior art, the invention realizes the technical effect of constructing the interaction mode among the pedestrians and carrying out the track prediction, and the prediction result has good robustness.
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FIG. 1 is an overall flow chart of a pedestrian trajectory prediction method based on an unmanned aerial vehicle aerial photography view angle according to the present invention;
FIG. 2 is a schematic diagram of a model framework structure of an embodiment of a pedestrian trajectory prediction method based on an unmanned aerial vehicle aerial photography view angle;
FIG. 3 is a schematic diagram of a track prediction live-action in which the solid lines are observed historical tracks, the dark dotted lines are actual future tracks, the light dotted lines are predicted future tracks, and two pedestrians on the right side of the graph (a) walk from right to left, and one pedestrian on the left side walks from left to right; in the figure (b), three pedestrians walk from right to left. The basic coincidence with the light-colored dotted line can be observed from the figure, which shows that the prediction effect of the patent is better
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
The overall idea of the invention is to realize the prediction of the pedestrian track by adopting the overlooking pedestrian video obtained based on the aerial photography of the unmanned aerial vehicle.
As shown in fig. 1, the method mainly comprises the following steps:
step 1: carrying out pedestrian track preprocessing in the pedestrian video aerial photographed by the unmanned aerial vehicle: the pedestrian video that unmanned aerial vehicle was taken photo by plane contains the pedestrian of a plurality of overlooking visual angles, uses existing target detection and target tracking method to fix a position the pedestrian fast, promptly: taking the central position of the target frame as the position of the pedestrian, and setting the track coordinates X of all observed pedestrians as X1,X2,…,XnExtracting two-dimensional position sequence of pedestrian, setting input sequence and prediction sequence length；
Step 2: and (3) carrying out pedestrian track coding: the relative position change of a single pedestrian trajectory between the previous frame and the next frame is expressed as:
Figure BDA0002844966000000041
encoding to fixed length motion vectors using long short term memory networks
Figure BDA0002844966000000042
Then coding the motion vector by using a long-short term memory network to obtain the track motion characteristics
Figure BDA0002844966000000043
And step 3: constructing a graph convolution network interaction model: using graph structure Gt＝(Vt,Et) Establishing an interactive model among pedestrians at the time t, and taking the pedestrians as a set V of vertexes in a graph structuretThe interaction relation among the pedestrians is a set E of edgestThe vertex V in each time pointtConnection relation E oftExpressed as adjacency matrix AtWill adjoin the matrix AtEdge of (1)
Figure BDA0002844966000000044
Weights assigned according to different distances
Figure BDA0002844966000000045
Expressed as:
Figure BDA0002844966000000046
characteristics of the path movement
Figure BDA0002844966000000047
Input features as vertices in graph convolution networks
Figure BDA0002844966000000048
Superposing two layers of graph convolution networks, and passing through two layers of GCN nodesConstructing output characteristics of the ith track
Figure BDA0002844966000000049
Output characteristics of pair-to-figure convolution network
Figure BDA00028449660000000410
Carrying out long-short term memory network coding to obtain the track interaction characteristics
Figure BDA00028449660000000411
And 4, step 4: in order to enable the graph convolution network to construct a good pedestrian track interaction relationship, the maximum mutual information method is used for realizing the mutual information between the local features and the global features of the maximum track interaction features, namely the maximum mutual information optimization is realized, and the specific process is as follows: firstly, making negative sample of convolution network input
Figure BDA00028449660000000412
Obtaining output by a graph convolution network
Figure BDA00028449660000000413
Simultaneous extraction of global features
Figure BDA0002844966000000051
The judger D is then trained so that it can output negative examples
Figure BDA0002844966000000052
Misjudging and matching the output Z of the positive sample, thereby training the loss function L of the discriminatorinf，LinfExpressed as:
Figure BDA0002844966000000053
through the training process, the extraction result of the graph convolution network is optimized;
and 5: and (3) carrying out track prediction: using long and short term memory network to characterize trajectory motion
Figure BDA0002844966000000054
And trajectory interaction features
Figure BDA0002844966000000055
Decoding is performed, a frame of two-dimensional pedestrian trajectory prediction is output, and it is determined whether the total output length reaches the prediction sequence length? If not, adding a new output frame into the input sequence, discarding the input of the first frame, if so, outputting the prediction sequence, thereby obtaining the prediction sequence with a certain time length and completing the track prediction.

Claims (1)

1. A pedestrian trajectory prediction method based on an unmanned aerial vehicle aerial photography view angle is characterized by specifically comprising the following steps:
step 1: carry out pedestrian's orbit preliminary treatment in the pedestrian video of unmanned aerial vehicle aerial photography, including fixing a position the pedestrian fast, the central point who gets the target frame promptly is pedestrian's position, establishes all orbit coordinates X of surveing the pedestrian and is X ═ X1,X2,…,Xn；
Step 2: and (3) carrying out pedestrian track coding: the relative position change of a single pedestrian trajectory between the previous frame and the next frame is expressed as:
Figure FDA0002844965990000011
encoding to fixed length motion vectors using long short term memory networks
Figure FDA0002844965990000012
Then using long-short term memory network to encode to obtain the trace motion characteristics
Figure FDA0002844965990000013
And step 3: constructing graph convolution network interaction: using graph structure Gt＝(Vt,Et) Establishing an interactive model among pedestrians at the time t, and taking the pedestrians as a set V of vertexes in a graph structuretThe interaction relation among the pedestrians is a set E of edgestEach one of themVertex V in time pointtConnection relation E oftExpressed as adjacency matrix AtWill adjoin the matrix AtEdge of (1)
Figure FDA0002844965990000014
Weights assigned according to different distances
Figure FDA0002844965990000015
Figure FDA0002844965990000016
Expressed as:
Figure FDA0002844965990000017
characteristics of the path movement
Figure FDA0002844965990000018
Input features V as vertices in graph convolution networksi tSuperposing two layers of graph convolution networks, and obtaining the output characteristic of the ith track through two layers of GCN structures
Figure FDA0002844965990000019
Output characteristics of pair-to-figure convolution network
Figure FDA00028449659900000110
Carrying out long-short term memory network coding to obtain the track interaction characteristics
Figure FDA00028449659900000111
And 4, step 4: the method for realizing the maximum mutual information between the local features and the global features of the track interaction features comprises the following specific processes: firstly, making negative sample of convolution network input
Figure FDA00028449659900000112
Figure FDA00028449659900000113
Obtaining output by a graph convolution network
Figure FDA00028449659900000114
Simultaneous extraction of global features
Figure FDA00028449659900000115
The judger D is then trained so that it can output negative examples
Figure FDA00028449659900000116
Misjudging and matching the output Z of the positive sample, thereby training the loss function L of the discriminatorinf，LinfExpressed as:
Figure FDA0002844965990000021
through the training process, the extraction result of the graph convolution network is optimized;
and 5: and (3) carrying out track prediction: using long and short term memory network to characterize trajectory motion
Figure FDA0002844965990000022
And trajectory interaction features
Figure FDA0002844965990000023
Decoding is performed, a frame of two-dimensional pedestrian trajectory prediction is output, and it is determined whether the total output length reaches the prediction sequence length? If not, adding a new output frame into the input sequence, discarding the input of the first frame, if so, outputting the prediction sequence, thereby obtaining the prediction sequence with a certain time length and completing the track prediction.
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