The DIVA Framework is a software framework designed to provide an architecture and a set of software modules which will facilitate the development of activity recognition analytics. The Framework is developed as a fully open source project on GitHub. The following links will help you get started with the framework:
DIVA Framework Main Documentation PageThe source for the framework documentation is maintained in the Github repository using Sphinx. A built version is maintained on ReadTheDocs at this link. A good place to get started in the documentation, after reading the Introduction is the UseCase section which will walk you though a number of typical use cases with the framework.
The DIVA Framework is based on KWIVER, an open source framework designed for building complex computer vision systems. The following links will help you learn more about KWIVER:
KWIVER Issue Tracker Submit any bug reports or feature requests for the KWIVER here. If there's any question about whether your issues belongs in the KWIVER or DIVA framework issues tracker, submit to the DIVA tracker and we'll sort it out..
An ActEV activity is defined to be “one or more people performing a specified movement or
interacting with an object or group of objects”. Activities are annotated by humans using a set of
annotation guidelines that specify how to perform the annotation and the criteria to determine if
the activity occurred. Each activity is formally defined by five elements:
Activity Name - A mnemonic handle for the activity
Activity Description - Textual description of the activity
Begin time rule definition - The specification of what determines the beginning time of the activity
End time rule definition - The specification of what determines the ending time of the activity
Required object type list - The list of objects systems are expected to identify for the activity. Note: this aspect of an activity not addressed by ActEV-PC.
Description and Example Chip Videos
Description: A person closing the door to a vehicle or facility.
Start: The event begins 1 s before the door starts to move.
End: The event ends after the door stops moving. People in cars who close the car door from within is a closing event if you can still see the person within the car. If the person is not visible once they are in the car, then the closing should not be annotated as an event.
Objects associated with the activity : Person; and Door or Vehicle
Description: A vehicle turning left or right is determined from the POV of the driver of the vehicle. The vehicle may not stop for more than 10 s during the turn.
Start: Annotation begins 1 s before vehicle has noticeably changed direction.
End: Annotation ends 1 s after the vehicle is no longer changing direction and linear motion has resumed. Note: This event is determined after a reasonable interpretation of the video.
Objects associated with the activity : Vehicle
Description: An object moving from person to vehicle.
Start: The event begins 2 s before the cargo to be loaded is extended toward the vehicle (i.e., before a person’s posture changes from one of “carrying” to one of “loading”).
End: The event ends after the cargo is placed into the vehicle and the person-cargo contact is lost. In the event of occlusion, it ends when the loss of contact is visible.
Objects associated with the activity: Person; and Vehicle
Activities for the ActEV evaluations
The ActEV evaluation will continue to add, modify, and exclude activities each evaluation cycle.
The table below provides a list of activities for ActEV Sequestered Data Leaderboard (SDL), ActEV TRECVID 2019, and ActEV Prize Challenge evaluations.
We will invite the top two teams on the SDL leaderboard to give oral presentations at the HADCV'20 workshop based on the CLI submission deadline of January 10th, 2020 at 12:00 noon. In addition, we are inviting the rest of the teams to present their work as posters at the workshop.
ActEV is a series of evaluations to accelerate the development of robust, multi-camera,
automatic activity detection algorithms for forensic and real-time alerting applications.
ActEV is an extension of the annual TRECVID
Surveillance Event Detection (SED) evaluation
where systems will also detect and track objects involved in the activities.
Each evaluation will challenge systems with new data, system requirements, and/or new activities. Currently we are running the ActEV Sequestered Data Leaderboard and ActEV TrecVID 2019 evaluations.
An ActEV activity is defined to be
“one or more people performing a specified movement or interacting with an object or group of objects”.
Activity detection technologies process extended video streams,
such as those from a CCTV camera, and
automatically detects all instances of the activity by:
(1) identifying the type of activity,
(2) producing a confidence score indicating the presence of instance,
(3) temporally localizing the instance by indicating the begin and end times,
and (4) optionally, detecting and tracking the objects (people, vehicles, objects) involved in the activity.
The ActEV evaluations are being conducted to assess the robustness of automatic activity detection for a multi-camera streaming video environment.
Everyone. Anyone who registers can submit to the evaluation server.
Register here and then based on the evaluation participants can either ran their activity detection software on their compute hardware and submit their system output to the ActEV Scoring Server or submited their runnable activity detection software to NIST using the Evaluation Commandline Interface. See the individual evaluation pages and evaluation plans for details.
Each ActEV evaluation uses a new video data set, changes the evaluation tasks, or adds/changes activities. The data will be
provided in MPEG-4 and AVI formatted files. See the individual evaluation pages for details.
Evaluation Metrics and Tools
The main scoring metrics will be based on
detection, temporal localization, and spatio-temporal localization using evaluation measures that include
the probability of
missed detection and rate of false alarm. See details in the evaluation plans of each evaluation.
Below you will find four example videos from our data sets. There are two example views each of indoor and outdoor.
ActEV Evaluation Tasks
Activity detection has been researched for many years and remains an unsolved computer vision challenge
that requires many capabilities beyond the current state of the art.
The ActEV series supports several evaluation tasks each escalating the difficulty by requiring more specific information from the system.
Presently, there are three evaluation tasks defined: 1) Activity Detection (AD), 2) Activity
and Object Detection (AOD), and (3) Activity and Object Detection and Tracking
(AODT). Each evaluation task is summarized below. For a full description of the evaluation tasks,
read the Evaluation Plan for each specific evaluation.
Activity Detection (AD)
For the Activity Detection task, given a target activity, a system automatically detects and temporally
localizes all instances of the activity. For a
system-identified activity instance to be evaluated as correct, the type of activity must be correct and
the temporal overlap must fall within a minimal requirement.
Activity and Object Detection (AOD)
For the Activity and Object Detection task, given a target activity, a system detects and temporally
localizes all instances of the activity and spatially detects/localizes the people and/or objects associated
with the target activity. For a system-identified instance to be scored as correct, it must meet the temporal
overlap criteria for the AD task and in addition meet the spatial overlap of the identified objects during
the activity instance.
Activity Object Detection and Tracking (AODT)
For the Activity Object Detection and Tracking task, given a target activity, a system detects and temporally
localizes all instances of the activity, spatio-temporally detects/localizes the people and/or objects
associated with the target activity, and properly assigns IDs the objects play in the activity. For a
system-identified instance to be scored as correct, it must meet the temporal overlap criteria and
spatio-temporal overlap of the objects for the AOD task and correctly assign the IDs to the objects as
described in the activity definition.