CN104519323A - Personnel and vehicle target classification system and method - Google Patents
Personnel and vehicle target classification system and method Download PDFInfo
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- CN104519323A CN104519323A CN201410829383.3A CN201410829383A CN104519323A CN 104519323 A CN104519323 A CN 104519323A CN 201410829383 A CN201410829383 A CN 201410829383A CN 104519323 A CN104519323 A CN 104519323A
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Abstract
The invention relates to a personnel and vehicle target classification system and method. The method comprises the following steps: (1) conducting physical partition on video according to setting byte amount to obtain a plurality of video blocks; (2) saving the partitioned video blocks at different child nodes in sequence; (3) conducting logic slicing on a video block according to the key frame of each video block to obtain a plurality of video slices; (4) analyzing each video slice to obtain a key value pair of the video slice; (5) conducting personnel and vehicle classification on the video slices according to the key value pairs obtained from analysis to obtain personnel target information and vehicle target information; (6) saving the personnel target information and the vehicle target information after classification in different catalogs respectively. Compared with the prior art, the method is easy to operate, efficient in processing, and effective in unziping.
Description
Technical field
The present invention relates to technical field of video monitoring, particularly a kind of people's car non-target classification system and method.
Background technology
Video monitoring data amount is huge, and along with the intensifying trend in high Qinghua, superelevation Qinghua, video monitoring data scale will increase with index rank; But, owing to lacking the method for the video data of magnanimity being carried out to effectively analysis fast, cause information utilization extremely low.
The efficient massive video analysis that develops into of cloud computing technology provides condition.But cloud platform is mainly towards the text data of process magnanimity now, has good effect in the field such as log analysis, Web Page Processing.When on this cloud platform to video data carry out and pedestrian's car target classification time there is following problem:
First, video data is non-structured data, contains a large amount of useful informations, occupies very large memory space, and in order to save every resource, usual video is all compressed.Therefore, when carrying out intellectual analysis to video, needing first to decompress to video, then intellectual analysis is carried out to the frame of video that decompress(ion) obtains.What namely process in cloud platform is frame of video, and usual data structure is all for text, directly can only process text.
Secondly, when data are stored in distributed memory system, data block can be carried out physics cutting by the size of data according to regulation, namely each data file is cut into the identical data block of every block size.And there is between frame of video relevance, when carrying out people's car target classification to video, need to detect the target in video, follow the tracks of, therefore random cutting can not be carried out according to byte-sized, otherwise can, because of can not find key frame, lacking the failed situation of the problem appearance decodings such as header file, finally cause carrying out video intelligent analysis.
Finally, although video data first can be cut according to approximately specifying size with video cutter, and then upload in distributed file system and carry out intellectual analysis, the method not only increases workload, time with massive video data, be difficult to operation especially face to face.
Summary of the invention
Technical problem to be solved by this invention is to provide that a kind of convenient operation, process are efficient, decompress(ion) effective people's car non-target classification system and method.
The technical scheme that the present invention solves the problems of the technologies described above is as follows: a kind of people's car objective classification method, comprises the following steps:
Step S1: by setting amount of bytes, video is carried out physical segmentation, obtain multiple video block;
Step S2: the video block of segmentation is stored in different child node by the sequencing of former video;
Step S3: carry out logic burst to this video block according to the key frame in each video block, obtains multiple video slicing;
Step S4, resolves each video slicing, obtains the key-value pair of this video slicing;
Step S5: according to resolving the key-value pair obtained, the classification of people's car being carried out to described video slicing, obtains personnel targets information and vehicle target information;
Step S6: under sorted personnel targets information and vehicle target information are stored in its different catalogue respectively.
The invention has the beneficial effects as follows: carry out logic burst with key frame, the treatment effeciency of video can be improved; The classification of people's car is efficient, closes contact by force, improve decoding efficiency and accuracy between frame of video.
On the basis of technique scheme, the present invention can also do following improvement.
Further, the specific implementation step of described step 3:
Step S3.1: read video block, set up index, the key frame in marking video block;
Step S3.2: read index, redefine original position and the end position of video slicing according to key frame;
Step S3.3: resolve video slicing, generates the key-value pair of this video slicing, and the key of described key-value pair is the frame number of frame of video, is worth for frame of video.
Adopt the beneficial effect of above-mentioned further scheme to be: to set up index, mark key frame, can accelerate the location of key frame, improves the efficiency of video slicing; Key is the frame number of frame of video, is worth the key-value pair for frame of video, improves the efficiency of people's car classification.
Further, the structure of described index is the skew amount of bytes of video frame number and frame of video.
Further, the specific implementation of described step S3.2:
Step S3.2.1: the original position determining video block, judges whether the original position of video block is key frame;
Step S3.2.2: if key frame, then determine that key frame is the start frame position of video block, if not key frame, then read the frame of video of forward video block successively, find key frame, and this key frame is defined as the start frame position of video slicing;
Step S3.2.3: the end position determining video block, judges whether the end position of video block is key frame;
Step S3.2.4: if key frame, then determine that key frame is the end frame position of video block, if not key frame, then read the frame of video of this video block video block rearward successively, find key frame, and this key frame is defined as the end frame position of video slicing.
Adopting the beneficial effect of above-mentioned further scheme to be: to avoid the unsuccessful situation of video decode caused because lacking header file, key frame etc., improving and being decoded into power.
Further, described step S3.2 is further comprising the steps of: by the starting and ending position redefining video slicing, is write as by video slicing and uses the streamed of Xuggl er process.
Further, the specific implementation of described step 4:
The specific implementation of described step 5:
Window scans the frame of video importing whole video slicing into one by one, and calculate the HOG feature of being lived image by window frame, HOG feature is sent in people's car grader and mate with the personnel targets information of setting and vehicle target information, when there being the HOG feature of coupling, be then divided into personnel targets information or vehicle target information transmission to memory module the HOG feature of correspondence.
The beneficial effect of above-mentioned further scheme is adopted to be: can distinguish personnel targets information and vehicle target information fast, classification effectiveness is high.
Another technical scheme that the present invention solves the problems of the technologies described above is as follows:
A kind of people's car non-target classification system, comprises segmentation module, memory module, logic burst module and people's car sort module;
Described segmentation module, for video being carried out physical segmentation by setting amount of bytes, obtains multiple video block;
Described memory module, for the video block of segmentation is stored in different child node sequentially, also for sorted people and Che target are stored in its different directories;
Described logic burst module, for carrying out logic burst according to the key frame in each video block to this video block, obtains multiple video slicing;
Described people's car sort module, for according to resolving the key-value pair obtained, carrying out the classification of people's car to described video slicing, obtaining personnel targets information and vehicle target information.
Described logic burst module comprises reading unit, positioning unit and resolution unit;
Described reading unit, for reading video block, sets up index, the key frame in marking video block;
Described positioning unit, for reading index, redefines original position and the end position of video slicing according to key frame;
Described resolution unit, for resolving video slicing, generates the key-value pair of this video slicing, and the key of described key-value pair is the frame number of frame of video, is worth for frame of video.
Described people's car sort module is provided with window and people's car grader, described window can scan the frame of video importing whole video slicing into one by one, and calculate the HOG feature of being lived image by window frame, HOG feature is sent in people's car grader and mate with the personnel targets information of setting and vehicle target information, when there being the HOG feature of coupling, be then divided into personnel targets information or vehicle target information transmission to memory module the HOG feature of correspondence.
The invention has the beneficial effects as follows: carry out logic burst with key frame, the treatment effeciency of video can be improved; The classification of people's car is efficient, closes contact by force, improve decoding efficiency and accuracy between frame of video.
Accompanying drawing explanation
Fig. 1 is the flow chart of a kind of people's car of the present invention objective classification method;
Fig. 2 is the module frame chart of a kind of people's car of the present invention non-target classification system;
Fig. 3 is the flow chart of logic sharding method;
Fig. 4 is the module frame of logic burst module.
In accompanying drawing, the list of parts representated by each label is as follows:
1, module is split, 2, memory module;
3, logic burst module, 31, reading unit, 32, positioning unit, 33, resolution unit;
4, people's car sort module.
Embodiment
Be described principle of the present invention and feature below in conjunction with accompanying drawing, example, only for explaining the present invention, is not intended to limit scope of the present invention.
As shown in Figure 1, a kind of people's car objective classification method, comprises the following steps:
Step S1: by setting amount of bytes, video is carried out physical segmentation, obtain multiple video block;
Step S2: the video block of segmentation is stored in different child node by the sequencing of former video;
Step S3: carry out logic burst to this video block according to the key frame in each video block, obtains multiple video slicing;
Step S4, resolves each video slicing, obtains the key-value pair of this video slicing;
Step S5: according to resolving the key-value pair obtained, the classification of people's car being carried out to described video slicing, obtains personnel targets information and vehicle target information;
Step S6: under sorted personnel targets information and vehicle target information are stored in its different catalogue respectively.
As shown in Figure 3, the specific implementation step of described step 3:
Step S3.1: read video block, set up index, the key frame in marking video block;
Step S3.2: read index, redefine original position and the end position of video slicing according to key frame;
Step S3.3: resolve video slicing, generates the key-value pair of this video slicing, and the key of described key-value pair is the frame number of frame of video, is worth for frame of video.
The structure of described index is the skew amount of bytes of video frame number and frame of video.
The specific implementation of described step S3.2:
Step S3.2.1: the original position determining video block, judges whether the original position of video block is key frame;
Step S3.2.2: if key frame, then determine that key frame is the start frame position of video block, if not key frame, then read the frame of video of forward video block successively, find key frame, and this key frame is defined as the start frame position of video slicing;
Step S3.2.3: the end position determining video block, judges whether the end position of video block is key frame;
Step S3.2.4: if key frame, then determine that key frame is the end frame position of video block, if not key frame, then read the frame of video of this video block video block rearward successively, find key frame, and this key frame is defined as the end frame position of video slicing.
Described step S3.2 is further comprising the steps of: by the starting and ending position redefining video slicing, and that is write as by video slicing with Xuggler process is streamed.
The specific implementation of described step 5:
Window scans the frame of video importing whole video slicing into one by one, and calculate the HOG feature of being lived image by window frame, HOG feature is sent in people's car grader and mate with the personnel targets information of setting and vehicle target information, when there being the HOG feature of coupling, be then divided into personnel targets information or vehicle target information transmission to memory module the HOG feature of correspondence.
As shown in Figure 2, a kind of people's car non-target classification system, comprises segmentation module 1, memory module 2, logic burst module 3 and people's car sort module 4;
Described segmentation module 1, for video being carried out physical segmentation by setting amount of bytes, obtains multiple video block;
Described memory module 2, for the video block of segmentation is stored in different child node sequentially, also for sorted people and Che target are stored in its different directories;
Described logic burst module 3, for carrying out logic burst according to the key frame in each video block to this video block, obtains multiple video slicing;
Described people's car sort module 4, for according to resolving the key-value pair obtained, carrying out the classification of people's car to described video slicing, obtaining personnel targets information and vehicle target information.
As shown in Figure 4, described logic burst module 3 comprises reading unit 31, positioning unit 32 and resolution unit 33;
Described reading unit 31, for reading video block, sets up index, the key frame in marking video block;
Described positioning unit 32, for reading index, redefines original position and the end position of video slicing according to key frame;
Described resolution unit 33, for resolving video slicing, generates the key-value pair of this video slicing, and the key of described key-value pair is the frame number of frame of video, is worth for frame of video.
Described people's car sort module 4 is provided with window and people's car grader, described window can scan the frame of video importing whole video slicing into one by one, and calculate the HOG feature of being lived image by window frame, HOG feature is sent in people's car grader and mate with the personnel targets information of setting and vehicle target information, when there being the HOG feature of coupling, be then divided into personnel targets information or vehicle target information transmission to memory module 2 the HOG feature of correspondence.
The present invention carries out logic burst with key frame, can improve the treatment effeciency of video; The classification of people's car is efficient, closes contact by force, improve decoding efficiency and accuracy between frame of video.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (9)
1. people's car objective classification method, is characterized in that, comprises the following steps:
Step S1: by setting amount of bytes, video is carried out physical segmentation, obtain multiple video block;
Step S2: the video block of segmentation is stored in different child node by the sequencing of former video;
Step S3: carry out logic burst to this video block according to the key frame in each video block, obtains multiple video slicing;
Step S4, resolves each video slicing, obtains the key-value pair of this video slicing;
Step S5: according to resolving the key-value pair obtained, the classification of people's car being carried out to described video slicing, obtains personnel targets information and vehicle target information;
Step S6: under sorted personnel targets information and vehicle target information are stored in its different catalogue respectively.
2. a kind of people's car objective classification method according to claim 1, is characterized in that, the specific implementation step of described step 3:
Step S3.1: read video block, set up index, the key frame in marking video block;
Step S3.2: read index, redefine original position and the end position of video slicing according to key frame;
Step S3.3: resolve video slicing, generates the key-value pair of this video slicing, and the key of described key-value pair is the frame number of frame of video, is worth for frame of video.
3. a kind of people's car objective classification method according to claim 2, it is characterized in that, the structure of described index is the skew amount of bytes of video frame number and frame of video.
4. a kind of people's car objective classification method according to claim 2, is characterized in that, the specific implementation of described step S3.2:
Step S3.2.1: the original position determining video block, judges whether the original position of video block is key frame;
Step S3.2.2: if key frame, then determine that key frame is the start frame position of video block, if not key frame, then read the frame of video of forward video block successively, find key frame, and this key frame is defined as the start frame position of video slicing;
Step S3.2.3: the end position determining video block, judges whether the end position of video block is key frame;
Step S3.2.4: if key frame, then determine that key frame is the end frame position of video block, if not key frame, then read the frame of video of this video block video block rearward successively, find key frame, and this key frame is defined as the end frame position of video slicing.
5. a kind of people's car objective classification method according to claim 4, it is characterized in that, described step S3.2 is further comprising the steps of: by the starting and ending position redefining video slicing, and that is write as by video slicing with Xuggler process is streamed.
6. a kind of people's car objective classification method according to claim 1, is characterized in that, the specific implementation of described step 5:
Window scans the frame of video importing whole video slicing into one by one, and calculate the HOG feature of being lived image by window frame, HOG feature is sent in people's car grader and mate with the personnel targets information of setting and vehicle target information, when there being the HOG feature of coupling, be then divided into personnel targets information or vehicle target information transmission to memory module the HOG feature of correspondence.
7. people's car non-target classification system, is characterized in that: comprise segmentation module (1), memory module (2), logic burst module (3) and people's car sort module (4);
Described segmentation module (1), for video being carried out physical segmentation by setting amount of bytes, obtains multiple video block;
Described memory module (2), for the video block of segmentation is stored in different child node sequentially, also for sorted people and Che target are stored in its different directories;
Described logic burst module (3), for carrying out logic burst according to the key frame in each video block to this video block, obtains multiple video slicing;
Described people's car sort module (4), for according to resolving the key-value pair obtained, carrying out the classification of people's car to described video slicing, obtaining personnel targets information and vehicle target information.
8. a kind of people's car non-target classification system according to claim 7, it is characterized in that, described logic burst module (3) comprises reading unit (31), positioning unit (32) and resolution unit (33);
Described reading unit (31), for reading video block, sets up index, the key frame in marking video block;
Described positioning unit (32), for reading index, redefines original position and the end position of video slicing according to key frame;
Described resolution unit (33), for resolving video slicing, generates the key-value pair of this video slicing, and the key of described key-value pair is the frame number of frame of video, is worth for frame of video.
9. a kind of people's car non-target classification system according to claim 7, it is characterized in that, described people's car sort module (4) is provided with window and people's car grader, described window can scan the frame of video importing whole video slicing into one by one, and calculate the HOG feature of being lived image by window frame, HOG feature is sent in people's car grader and mate with the personnel targets information of setting and vehicle target information, when there being the HOG feature of coupling, then the HOG feature of correspondence be divided into personnel targets information or vehicle target information transmission to memory module (2).
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Application publication date: 20150415 |