āĻāĻ āĻāĻŦā§āĻāĻāĻ¨ā§° āĻŦāĻŋāĻˇā§ā§
With the vast development of Internet capacity and speed, as well as wide adop- tion of media technologies in peopleâs daily life, a large amount of videos have been surging, and need to be efficiently processed or organized based on interest. The human visual perception system could, without difficulty, interpret and r- ognize thousands of events in videos, despite high level of video object clutters, different types of scene context, variability of motion scales, appearance changes, occlusions and object interactions. For a computer vision system, it has been be very challenging to achieve automatic video event understanding for decades. Broadly speaking, those challenges include robust detection of events under - tion clutters, event interpretation under complex scenes, multi-level semantic event inference, putting events in context and multiple cameras, event inference from object interactions, etc. In recent years, steady progress has been made towards better models for video event categorisation and recognition, e. g. , from modelling events with bag of spatial temporal features to discovering event context, from detecting events using a single camera to inferring events through a distributed camera network, and from low-level event feature extraction and description to high-level semantic event classification and recognition. Nowadays, text based video retrieval is widely used by commercial search engines. However, it is still very difficult to retrieve or categorise a specific video segment based on their content in a real multimedia system or in surveillance applications.
With the vast development of Internet capacity and speed, as well as wide adop- tion of media technologies in peopleâs daily life, a large amount of videos have been surging, and need to be efficiently processed or organized based on interest. The human visual perception system could, without dif...
āĻ āĻ§āĻŋāĻ āĻŦāĻŋāĻāĻžā§°āĻŋ āĻĒāĻžāĻāĻ
āĻāĻ āĻāĻŦā§āĻāĻāĻ¨āĻ āĻŽā§āĻ˛ā§āĻ¯āĻžāĻāĻāĻ¨ āĻā§°āĻ
āĻāĻŽāĻžāĻ āĻāĻĒā§āĻ¨āĻžā§° āĻŽāĻ¤āĻžāĻŽāĻ¤ āĻāĻ¨āĻžāĻāĻāĨ¤
āĻĒāĻĸāĻŧāĻžā§° āĻ¨āĻŋāĻ°ā§āĻĻā§āĻļāĻžā§ąāĻ˛ā§
āĻ¸ā§āĻŽāĻžā§°ā§āĻāĻĢâāĻ¨ āĻā§°ā§ āĻā§āĻŦāĻ˛ā§āĻ
Android āĻā§°ā§ iPad/iPhoneā§° āĻŦāĻžāĻŦā§ Google Play Books āĻāĻĒāĻā§ āĻāĻ¨āĻˇā§āĻāĻ˛ āĻā§°āĻāĨ¤ āĻ āĻ¸ā§āĻŦāĻ¯āĻŧāĻāĻā§āĻ°āĻŋāĻ¯āĻŧāĻāĻžā§ąā§ āĻāĻĒā§āĻ¨āĻžā§° āĻāĻāĻžāĻāĻŖā§āĻā§° āĻ¸ā§āĻ¤ā§ āĻāĻŋāĻāĻ āĻšāĻ¯āĻŧ āĻā§°ā§ āĻāĻĒā§āĻ¨āĻŋ āĻ¯'āĻ¤ā§ āĻ¨āĻžāĻĨāĻžāĻāĻ āĻ¤'āĻ¤ā§āĻ āĻā§āĻ¨ā§ āĻ
āĻĄāĻŋāĻ
'āĻŦā§āĻ āĻ
āĻ¨āĻ˛āĻžāĻāĻ¨ āĻŦāĻž āĻ
āĻĢāĻ˛āĻžāĻāĻ¨āĻ¤ āĻļā§āĻ¨āĻŋāĻŦāĻ˛ā§ āĻ¸ā§āĻŦāĻŋāĻ§āĻž āĻĻāĻŋāĻ¯āĻŧā§āĨ¤
āĻ˛ā§āĻĒāĻāĻĒ āĻā§°ā§ āĻāĻŽā§āĻĒāĻŋāĻāĻāĻžā§°
āĻāĻĒā§āĻ¨āĻŋ āĻāĻŽā§āĻĒāĻŋāĻāĻāĻžā§°ā§° ā§ąā§āĻŦ āĻŦā§āĻ°āĻžāĻāĻāĻžā§° āĻŦā§āĻ¯ā§ąāĻšāĻžā§° āĻā§°āĻŋ Google PlayāĻ¤ āĻāĻŋāĻ¨āĻž āĻ
āĻĄāĻŋāĻ
'āĻŦā§āĻāĻ¸āĻŽā§āĻš āĻļā§āĻ¨āĻŋāĻŦ āĻĒāĻžā§°ā§āĨ¤
āĻ-ā§°ā§āĻĄāĻžā§° āĻā§°ā§ āĻ
āĻ¨ā§āĻ¯ āĻĄāĻŋāĻāĻžāĻāĻ
Kobo eReadersā§° āĻĻā§°ā§ āĻ-āĻāĻŋā§āĻžāĻāĻšā§ā§° āĻĄāĻŋāĻāĻžāĻāĻāĻ¸āĻŽā§āĻšāĻ¤ āĻĒā§āĻŋāĻŦāĻ˛ā§, āĻāĻĒā§āĻ¨āĻŋ āĻāĻāĻž āĻĢāĻžāĻāĻ˛ āĻĄāĻžāĻāĻ¨āĻ˛âāĻĄ āĻā§°āĻŋ āĻ¸ā§āĻāĻā§ āĻāĻĒā§āĻ¨āĻžā§° āĻĄāĻŋāĻāĻžāĻāĻāĻ˛ā§ āĻ¸ā§āĻĨāĻžāĻ¨āĻžāĻ¨ā§āĻ¤ā§°āĻŖ āĻā§°āĻŋāĻŦ āĻ˛āĻžāĻāĻŋāĻŦāĨ¤ āĻ¸āĻŽā§°ā§āĻĨāĻŋāĻ¤ āĻ-ā§°āĻŋāĻĄāĻžā§°āĻ˛ā§ āĻĢāĻžāĻāĻ˛āĻā§ āĻā§āĻ¨ā§āĻā§ āĻ¸ā§āĻĨāĻžāĻ¨āĻžāĻ¨ā§āĻ¤ā§° āĻā§°āĻŋāĻŦ āĻāĻžāĻ¨āĻŋāĻŦāĻ˛ā§ āĻ¸āĻšāĻžāĻ¯āĻŧ āĻā§āĻ¨ā§āĻĻā§ā§°āĻ¤ āĻĨāĻāĻž āĻ¸āĻŦāĻŋāĻļā§āĻˇ āĻ¨āĻŋā§°ā§āĻĻā§āĻļāĻžā§ąāĻ˛ā§ āĻāĻžāĻāĻāĨ¤
