A method for updating an online self-learning event detection model, which is applicable in an electronic device, the method comprises detecting at least one target alerting event generated by a current event detection model, presenting to a user the at least one target alerting event, and the user providing an event result for each target alerting event based on the at least one presented target alerting event; obtaining the event result for each target alerting event provided by the user based on the at least one presented target alerting event; determining whether a target alerting event for which an event result has been provided by the user satisfies a predetermined update condition, and if so, training a target event detection model based on the at least one target alerting event for which an event result has been provided by the user and corresponding event result, and predetermined training samples, wherein the predetermined training samples comprise at least one of: a plurality of predefined reference events and corresponding event results, and a plurality of historical alerting events that have been confirmed by the user and corresponding event results, and wherein the predefined update condition comprises: the number of target alerting events for which event results have been provided by the user reaching a predetermined threshold, or a target alerting event for which an event result has been provided by the user occurring within a predefined period of time; wherein the current event detection model and the target event detection model are used to perform online self-learning based on input learning data, which includes a video or an image, the method characterized by: through online learning of a status of a vehicle departure from a lane, the current event detection model and the target event detection model distinguish an intended lane departure due to an intended lane-changing operation by a driver from an unintended lane departure due to an unintended action of the driver, generate an alert for the unintended lane departure based on results of the learning to generate an alerting event; obtaining verification samples, and determining whether the false alert rate of the target event detection model is lower than that of the current event detection model based on the verification samples, wherein, the verification samples comprise at least one of: a plurality of predefined reference events and corresponding event results, one or more historical alerting events for which one or more event results have been provided by the user and one or more corresponding event result, at least one target alerting event for which at least one event result has been provided by the user and at least one corresponding event result; and if so, replacing the current event detection model with the target event detection model (104, 205). detecting at least one target alerting event generated by a current event detection model, presenting to a user the at least one target alerting event, and the user providing an event result for each target alerting event based on the at least one presented target alerting event; obtaining the event result for each target alerting event provided by the user based on the at least one presented target alerting event; determining whether a target alerting event for which an event result has been provided by the user satisfies a predetermined update condition, and if so, training a target event detection model based on the at least one target alerting event for which an event result has been provided by the user and corresponding event result, and predetermined training samples, wherein the predetermined training samples comprise at least one of: a plurality of predefined reference events and corresponding event results, and a plurality of historical alerting events that have been confirmed by the user and corresponding event results, and wherein the predefined update condition comprises: the number of target alerting events for which event results have been provided by the user reaching a predetermined threshold, or a target alerting event for which an event result has been provided by the user occurring within a predefined period of time; wherein the current event detection model and the target event detection model are used to perform online self-learning based on input learning data, which includes a video or an image, the method characterized by: through online learning of a status of a vehicle departure from a lane, the current event detection model and the target event detection model distinguish an intended lane departure due to an intended lane-changing operation by a driver from an unintended lane departure due to an unintended action of the driver, generate an alert for the unintended lane departure based on results of the learning to generate an alerting event; obtaining verification samples, and determining whether the false alert rate of the target event detection model is lower than that of the current event detection model based on the verification samples, wherein, the verification samples comprise at least one of: a plurality of predefined reference events and corresponding event results, one or more historical alerting events for which one or more event results have been provided by the user and one or more corresponding event result, at least one target alerting event for which at least one event result has been provided by the user and at least one corresponding event result; and if so, replacing the current event detection model with the target event detection model (104, 205).