US 10,949,673 B2Granted

Target detection method and device

AssigneeHangzhou Hikvision Digital Technology Co., Ltd.

InventorsPing Yang, Shiliang Pu, Di Xie

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Abstract

Embodiments of the present application disclose a target detection method and device, and relate to the technical field of video processing. The method comprises: obtaining an image sequence to be detected from a video to be detected according to an image sequence determining algorithm based on video timing (S101), extracting a first CNN feature of the image sequence to be detected based on a pre-trained CNN model, performing feature fusion on the first CNN feature based on a second CNN feature to obtain a first fused CNN feature of the image sequence to be detected (S102); inputting the first fused CNN feature into the first-level classifier, and obtaining first candidate target regions of the image sequence to be detected from an output of the first-level classifier (S103); determining a first input region of the second-level classifier based on the first candidate target regions (S104); obtaining a third CNN feature of the first input region based on the first fused CNN feature (S105); inputting the third CNN feature into the second-level classifier, and obtaining a target detection result for the image sequence to be detected based on the output of the second-level classifier (S106).

Claims18

Dependent claims

A target detection method, comprising:

obtaining, from a video to be detected, an image sequence to be detected according to an image sequence determining algorithm based on video timing;
extracting a first CNN feature of the image sequence to be detected based on a pre-trained Convolutional Neural Network CNN model, and performing feature fusion on the first CNN feature of the image sequence based on a second CNN feature to obtain a first fused CNN feature of the image sequence to be detected, wherein, the second CNN feature is a CNN feature of a detected image sequence in the video to be detected, and the CNN model comprises a first-level classifier and a second-level classifier, wherein, the first-level classifier is a classifier obtained by training a CNN based on a second fused CNN feature of a sample image sequence and a labeled region in the sample image sequence where a target is located, the second-level classifier is a classifier obtained by training the CNN based on the second fused CNN feature, the labeled region, and an output of the first-level classifier, and the sample image sequence is an image sequence obtained from a sample video according to the image sequence determining algorithm;
inputting the first fused CNN feature into the first-level classifier, and obtaining, from the output of the first-level classifier, first candidate target regions in the image sequence to be detected;
determining a first input region of the second-level classifier based on the first candidate target regions;
obtaining a third CNN feature of the first input region based on the first fused CNN feature;
inputting the third CNN feature into the second-level classifier, and obtaining a target detection result for the image sequence to be detected based on an output of the second-level classifier.

3 more elements

A target detection device, comprising:

a sequence obtaining module, configured for obtaining, from a video to be detected, an image sequence to be detected according to an image sequence determining algorithm based on video timing;
a feature extracting module, configured for extracting a first CNN feature of the image sequence to be detected based on a pre-trained Convolutional Neural Network CNN model, wherein the CNN model comprises a first-level classifier and a second-level classifier, wherein, the first-level classifier is a classifier obtained by training a CNN based on a second fused CNN feature of a sample image sequence and a labeled region in the sample image sequence where a target is located, the second-level classifier is a classifier obtained by training the CNN based on the second fused CNN feature, the labeled region, and an output of the first-level classifier, and the sample image sequence is an image sequence obtained from a sample video according to the image sequence determining algorithm;
a first feature obtaining module, configured for performing feature fusion on the first CNN feature of the image sequence based on a second CNN feature to obtain a first fused CNN feature of the image sequence to be detected, wherein, the second CNN feature is a CNN feature of a detected image sequence in the video to be detected;
a region obtaining module, configured for inputting the first fused CNN feature into the first-level classifier, and obtaining, from the output of the first-level classifier, first candidate target regions in the image sequence to be detected;
a region determining module, configured for determining a first input region of the second-level classifier based on the first candidate target regions;
a second feature obtaining module, configured for obtaining a third CNN feature of the first input region based on the first fused CNN feature;
a result obtaining module, configured for inputting the third CNN feature into the second-level classifier, and obtaining a target detection result for the image sequence to be detected based on an output of the second-level classifier.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/347,626Filed 7 Nov 2017
Priority: CN 201610980457.28 Nov 2016
Published: US 2019/0347485 A114 Nov 2019
Granted: 16 Mar 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification16 codes

G06V 10/82
Using pattern recognition or machine learning: using neural networks
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…
G06F 18/24317
Relating to the number of classes: piecewise classification, i.e. whereby each classification requires several discriminant rules
G06F 18/253
Fusion techniques: of extracted features