Object Tracking

Object tracking types

gvatrack is an object tracking element, typically inserted into a video analytics pipeline right after the object detection element gvadetect and can work in the following modes as specified by the tracking-type property:

tracking-type	Max detection interval (inference-interval property in gvadetect)	Algorithm uses detected coordinates and trajectory extrapolation	Algorithm uses image data	Notes
short-term-imageless	<= 5	Yes	No	Assigns a unique id to objects and generates object position for frames on which object detection was skipped. Fast algorithm that extrapolates object trajectory from previous frame(s) without access to image data.
zero-term	1 (every frame)	Yes	Yes	Assigns a unique id to objects, and requires object detection run on every frame. Takes into account object trajectory as well as a color histogram of object image data.
zero-term-imageless	1 (every frame)	Yes	No	Assigns a unique id to objects, and requires object detection run on every frame. Fastest algorithm as based on comparing object coordinates on current frame with objects trajectory on previous frames.

Additional configuration

Additional configuration parameters for object tracker can be passed via the config property of gvatrack. The config property accepts a comma separated list of KEY=VALUE parameters. The supported parameters are described below:

Tracking per class

Configurable via tracking_per_class parameter. It specifies whether the class label is considered for updating the object_id of an object or not. When set to true, a new tracking ID will be assigned to an object when the class label changes. When set to false, the tracking ID will be retained, based on the position of the bounding box, even if the class label of the object changes due to model inaccuracy. The default value is true.

Example:

... gvatrack config=tracking_per_class=false ...

Maximum number of objects

Configurable via max_num_objects parameter. It specifies the maximum number of objects that the object tracker will track. On devices with less computing power, tracking a smaller number of objects can reduce compute and increase throughput.

Example:

... gvatrack config=max_num_objects=20 ...

Sample

Refer to the vehicle_pedestrian_tracking sample for a pipeline with gvadetect, gvatrack, and gvaclassify elements.

How to read object unique id

The following code example iterates all objects detected or tracked on the current frame and prints object unique id and bounding box coordinates.

#include "video_frame.h"

void PrintObjects(GstBuffer *buffer) {
    GVA::VideoFrame video_frame(buffer);
    std::vector<GVA::RegionOfInterest> regions = video_frame.regions();
    for (GVA::RegionOfInterest &roi : regions) { // iterate objects
        int object_id = roi.object_id(); // get unique object id
        auto bbox = roi.rect(); // get bounding box information
        std::cout << "Object id=" << object_id << ", bounding box: " << bbox.x << "," << bbox.y << "," << bbox.w << "," << bbox.h << "," << std::endl;
    }
}

Performance considerations

Object tracking can help improve performance of both object detection (gvadetect) and object classification (gvaclassify`) elements

• Object detection: short-term-imageless tracking types enable reducing object detection frequency by setting the inference-interval property in the gvadetect element.

• Object classification: if an object was classified by gvaclassify on frame N, you can skip classification of the same object for several next frames N+1,N+2,… and reuse last classification result from frame N. Reclassification interval is controlled by the reclassify-interval property in the gvaclassify element.

See the sample pipeline below:

gst-launch-1.0 \
... ! \
decodebin3 ! \
gvadetect model=$DETECTION_MODEL inference-interval=10 ! \
gvatrack tracking-type=short-term-imageless ! \
gvaclassify model=$AGE_GENDER_MODEL reclassify-interval=30 ! \
gvaclassify model=$EMOTION_MODEL reclassify-interval=15 ! \
gvaclassify model=$LANDMARKS_MODEL ! \
...

It detects faces every 10th frame and tracks faces position for the next 9 frames. The age and gender classification is updated every second, the emotion classification is updated twice a second, and the landmark points are updated every frame.